Can a preview of an upcoming curve mitigate the effects of cognitive load on expert and non-expert drivers’ vehicle control?

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M. Celic, J. Billington, N. Merat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9449373/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The lack of theoretical and empirical coherence makes it difficult to ascertain how cognitive load, induced by a cognitive secondary activity, impacts the driving performance of non-expert and expert drivers. Even less is known about strategies that could mitigate potential impairments, precluding the development of effective countermeasures. Fourteen UK advanced police drivers (experts), and twenty experienced non-professional drivers (non-experts), were recruited for this study to examine whether a road sign previewing an upcoming curve could reduce the effects of cognitive load on driving performance. Differences between experts and non-experts were observed only in longitudinal control, while cognitive load primarily affected lateral control. When the road sign was present, experts reduced their speed both when approaching and negotiating curves, whereas non-experts slowed only within the curve. Although the road sign had a minimal impact on lane deviations when approaching curves under cognitive load, it effectively reduced deviations and improved steering smoothness in curves, irrespective of cognitive load. These findings underscore the task-specific nature of driving expertise and suggest that anticipatory visual cues can enhance safety and performance of expert and non-expert drivers even in situations when they are cognitively loaded. curve negotiation curve preview directional cue cognitive load driving expertise Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Highlights • Expert drivers reach higher minimum and mean speed than non-expert drivers. • Cognitive load does not affect experts’ and non-experts’ driving speed. • Cognitive load reduces steering smoothness and increases small steering corrections. • Cognitive load decreases lane deviations only in the absence of curve preview. • Curve preview reduces driving speed and lane deviations and increases steering smoothness. Introduction It is well established that environmental and road-related factors, such as limited visibility or sharp road curvature, increase driving demands, especially when combined with high travel speeds (Fuller, 2005 ; Lappi, 2022 ) or inadequate driver skills (Lehtonen et al., 2014 ). The pervasive integration of modern in-vehicle technologies adds to these demands by occupying drivers’ cognitive resources and increasing their cognitive load. While driving skill is critical for safe vehicle control (Beanland & Wynne, 2019 ; Clarke et al., 2006 ; Lappi, 2022 ; Pammer & Blink, 2018 ; Pammer et al., 2018 ), a lack of theoretical and empirical coherence in this context makes it difficult to ascertain how skill interacts with non-driving-related cognitive distractions to influence performance. Some researchers attribute better performance in skilled drivers to experience-based development of perceptual and cognitive skills (e.g., Drummond, 1989 ; Harrison, 1999 ; Sivak, 1981 ). However, findings from studies on expert drivers, such as advanced police drivers, driving instructors, or racing drivers, suggest that such driving performance requires more than experience alone (Crundall et al., 2003 ; Crundall et al., 2005 ; Johnston & Scialfa, 2016 ; Land & Tatler, 2001 ; Lappi et al., 2017 ; Pammer & Blink, 2018 ). Although skill automatization has been proposed as a key mechanism for coping with demanding traffic situations (Engström et al., 2017 ; Fuller, 2005 ), driving does not rely solely on either automatic or controlled processing. Rather, it reflects a dynamic interplay between the two, with their relative contribution shifting based on both driver skill level and situational demands (Schneider & Shiffrin, 1977; Fisk & Schneider, 1984). While expert drivers are generally expected to possess more automatized skills, typically developed through extensive driving experience and deliberate training (Ericsson et al., 1993 ; Pammer et al., 2018 ), it remains unclear whether this advantage consistently buffers against the added strain of concurrent cognitive tasks. 1.1 Cognitive load, driving expertise, and curve negotiation Most theoretical models of human information processing and task performance concur that humans have limited processing capacity which, when exceeded by task demands, leads to performance decrements (e.g., Broadbent, 1958 ; Kahneman, 1973 ; Wickens, 1984 ). This is particularly evident when cognitive resources are divided across two or more simultaneous tasks, with the level of resources required to perform such concurrent activities being referred to as cognitive load (Engström et al., 2017 ). According to Engström and colleagues ( 2017 ), for a task such as driving, cognitive load primarily affects activities that require cognitive control and resources, while leaving resource-free automatized skills largely unaffected. The degree of skill automatization, acquired through repeated experience with a task, leads to identical driving situations imposing varying demands on novice, experienced, and expert drivers. Drivers with more automatized skills are, therefore, believed to demonstrate superior performance even under cognitive load, compared to their less skilled counterparts (Engström et al., 2017 ). For instance, cognitively loaded experienced drivers, when compared to less experienced/novice drivers, tend to resort to their automatically controlled optimal speed, effectively compensating for increased task demands (Liao et al., 2018 ). Similarly, advanced paramedic drivers exhibit the lowest crash rates, despite driving at high speeds while managing various visual and cognitive distractions (Symmons et al., 2005 ). In terms of lateral vehicle control, our previous study (Celic et al., 2024 ) found that expert racing drivers adhered more closely to the optimal driving line, while experienced drivers deviated more when cognitively loaded. These findings align with research suggesting that drivers tend to steer where they look (e.g., Wilkie et al., 2010 ), and that cognitive load narrows gaze toward the road ahead, leading to more frequent micro-steering corrections and reduced lane deviations (Engström et al., 2017 ; Lehtonen et al., 2012 ; Li et al., 2018 ; Reimer, 2009 ; Wang et al., 2014 ). While such effects are typically observed in non-expert drivers, experts, who rely on peripheral vision to gather near-road information for immediate steering control without expending cognitive resources (Land & Horwood, 1995 ; Summala et al., 1996 ), may not exhibit an increase in small steering corrections under cognitive load. This strategy allows them to allocate more resources to trajectory planning, spending more time looking far ahead (Crundall et al., 2003 ; Konstantopoulos et al., 2010 ; Land & Tatler, 2001 ; Lappi et al., 2017 ; Muttart et al., 2013 ; Tuhkanen et al., 2019 ; van Leeuwen et al., 2017 ), and adjusting speed and steering in alignment with anticipated road curvature (He & Donmez, 2020; Stahl et al., 2019 ). Consequently, although the visual narrowing caused by cognitive load might not disrupt expert drivers’ use of near-road information for steering corrections, it might interfere with their ability to sample and process far-road information, potentially leading to more abrupt and less smooth steering. Yet, given the scarcity of studies specifically addressing how cognitive load affects expert drivers’ performance, further research is needed to clarify these effects. 1.2 Use of road signs as directional cues in curve negotiation Road signs serve as critical environmental cues, helping drivers anticipate curves and adjust their speed, especially on approach to curves with limited sight distance (Campbell et al., 2012 ; Charlton, 2004 ; Costa et al., 2022 ; Crundall & Underwood, 2001 ; Vos et al., 2021 ). Studies using a priming paradigm have consistently shown that even brief exposures to traffic signs can automatically prompt faster reactions to subsequent road scenes and facilitate curve anticipation (Crundall & Underwood, 2001 ; Koyuncu & Amado, 2008 ). However, their efficacy appears to depend on the type of road sign (Charlton, 2004 ; Charlton, 2006 ; Koyuncu & Amado, 2008 ), its location (Koyuncu & Amado, 2008 ), as well as the driving speed (Charlton, 2004 ; Koyuncu & Amado, 2008 ). While experienced drivers benefit more from road signs than novices (Crundall & Underwood, 2001 ), trained and professional male drivers demonstrate the highest levels of comprehension of symbolic road signs (Wontorczyk & Gaca, 2021 ). These findings support the notion that drivers interpret road signs by drawing on similar past experiences (Stahl et al., 2014 ; Stahl et al., 2019 ), with their skill level influencing how quickly they are encoded in complex situations (Lappi, 2022 ). Advanced police drivers, for instance, not only process a broader range of relevant stimuli than age- and experience-matched controls and novices, but also do so more quickly (Crundall et al., 2003 ). This is most likely due to their well-developed mental models of the driving environment, which are used to direct visual search (Crundall et al., 2005 ; Underwood et al., 2002 ; Underwood et al., 2003 ), manage speed (Walker et al., 2009 ), and guide steering (Tuhkanen et al., 2019 ). Environmental cues, including road signs, are thought to activate mental models that encompass causal and temporal relationships, also providing predictions about future developments of the situation (Durso et al., 2007 ; Mühl et al., 2020 ). The activation of these models relies on automatized processes, making them less susceptible to cognitive load (Mühl et al., 2020 ). However, Baumann and colleagues ( 2008 ) found that under cognitive load, road signs preceding obstacles were less effective at reducing speed and increasing time to collision, when compared to undistracted driving conditions. Conversely, Charlton ( 2004 ) showed that curve warnings emphasizing perceptual features of the curve (e.g., direction or severity) were effective in reducing the speed of cognitively loaded experienced drivers both when approaching and negotiating curves. While their design involved different combinations of road signs, including explicit speed instructions, such findings at least partially align with evidence that knowledge of environmental shape is key to successful navigation (McNamara, 2013 ). Although research suggests that road signs can aid implicit information processing (Charlton, 2004 ) and reduce uncertainty (Mühl et al., 2020 ; Mushtaq et al., 2011 ), it remains unclear whether road signs that clearly reinforce spatial information by indicating the upcoming curve, its direction, and degree of turning can equally support the performance of both expert and non-expert drivers when negotiating geometrically challenging curves under conditions of high speed and cognitive load. 1.3 Study aim and hypotheses The aim of this study was to examine if a road sign informing drivers of an upcoming curve would alter the performance of cognitively loaded expert and non-expert drivers while approaching and negotiating curves. Guided by theoretical models and existing literature, the study tested the following hypotheses: Both groups of drivers will resort to their optimal speed (Engström et al., 2017 ; Lehtonen et al., 2014 ; Liao et al., 2018 ) and increase steering corrections (Engström et al., 2017 ; Kountouriotis et al., 2015 ; Li et al., 2018 ) under cognitive load, particularly when approaching curves. 2. While both groups of drivers are expected to decrease their speed in response to the road sign (Crundall & Underwood, 2001 ; Wontorczyk & Gaca, 2021 ), this will be particularly evident among expert drivers when approaching curves, due to more accurate mental model of the curve ahead. 3. While the road sign is not expected to mitigate the adverse effects of cognitive load on steering corrections and lane deviations, for either group, it will increase expert drivers’ steering smoothness when approaching and negotiating curves, even under secondary task conditions, as the sign is expected to aid the prediction of the trajectory within a few seconds. 4. Reflecting the automatic nature of road sign processing (e.g., Charlton, 2004 ), the presence of such a cue is expected to support expert drivers' automatized driving skills, enabling them to maintain better secondary task performance. Method Participants Following earlier research on driving expertise (Celic et al., 2024 ), two groups of male participants – expert drivers ( n 1 = 14; M age = 43.14, SD = 6.98) and non-expert drivers ( n 2 = 20; M age = 38.05, SD = 5.77) – took part in this study. Non-expert drivers were experienced non-professional drivers, recruited using the University of Leeds Driving Simulator (UoLDS) database. The expert driver group included UK advanced police drivers and firefighters, all of whom regularly drove during their work shift. Both groups were selected using identical criteria. However, the expert drivers had also completed an advanced driver training course (covering visual scanning, hazard perception, anticipation, and vehicle control, e.g., pursuit and emergency response driving) and held a UK advanced driving permit for at least 5 years ( M = 11.79, SD = 6.73). All participants drove more than 10000 miles per year ( M Non−experts = 12794.61, SD = 2075.12; M Experts = 17620.22, SD = 6204.29) and had a valid driving license for more than 15 years ( M Non−experts = 19.95, SD = 6.41; M Experts = 24.64, SD = 6.40). Informed consent was obtained from all participants who responded to the advert and they were compensated with £30 in cash. The project was approved by the School of Psychology Research Ethics Committee, University of Leeds (PSYC-622). Apparatus The study was conducted at the UoLDS, a Jaguar S-Type cabin encased by a spherical projection dome, with a 300° projection angle and 8 degrees of freedom motion system. Each experimental session included a 10-minute familiarization drive and a 20-minute main drive. All drives took place on a one-lane, 10-meter-wide road featuring identical curved segments, each preceded and followed by long straight sections. The characteristics of the curves were held constant throughout the experiment. Specifically, all curves were 432-meter-long hairpin curves with a consistent radius of 137 meters and no vertical curvature. The curve approach tangent was a 756-meter-long straight segment while the exit tangent was a 504-meter-long straight segment. To separate these curves, every exit tangent was followed by a 1500-meter-long filler section where no data was collected. The overall road design and curve characteristics were based on a previous study (Celic et al., 2024 ), where these curves were identified as the most challenging for both non-expert and expert racing drivers. To minimize the risk of accidents and associated data loss, while still ensuring high-speed driving, vehicle speed was limited to 70 mph (112.65 kph), and drivers were instructed to reach this speed while approaching the curve. This value was chosen as it was close to the maximum speed at which the curve could be safely negotiated (75 mph). The simulated environment was set under consistent daytime lighting conditions across all trials and no other road objects, or oncoming traffic were present during the drive. Experimental design The study followed a mixed model design and included one between-subject factor of Driving expertise (Experts, Non-experts) and two within-subject factors of Road sign (No sign, Sign), and Cognitive load (No task, 2-back task). Every participant drove through 8 hairpin curves, half of which were curved towards the left, and half to the right to control for the curve direction. Therefore, the experimental drive included four curve pairs presented under different combinations of the two within-subject factors, with every curve being separated by a filler segment. The assignment of experimental conditions (Baseline; 2-back task only; Road sign only; Road sign combined with 2-back task) was counterbalanced across participants to control for order and learning effects. The purpose of the road sign was to provide the preview of the upcoming curve, including information about its direction and the approximate degree of the required turn. It was presented for 1 second within the driving scene, 410 meters before the curve entry. The onset and duration of the projection were based on earlier research indicating that the stimulus needs to be presented early and long enough for drivers to perceive it, particularly when driving at high speeds (e.g., Crundall & Underwood, 2001 ; Koyuncu & Amado, 2008 ). The onset of the projection was therefore specifically defined to ensure that the sign was perceived during the approach tangent, prior to both entering the curve and the onset of the secondary cognitive task. This design allowed the isolation of the effects of cognitive load on the processing and use of the sign information, rather than on its initial perception, and enabled the examination of changes in driving performance during both the approach and the negotiation of the low-visibility hairpin curve. The sign was shown in the standard order sign format, which is a white circle with a red border and a black symbol. Studies have found that, compared to other types of road signs, such signs prime the road scenes better due to their direct relationship with the behavioral response (Crundall & Underwood, 2001 ). The initial size of the sign was 96 x 96 pixels, and both its appearance and the information it conveyed remained constant throughout the experiment (Fig. 1 ), as the degree of curve turning did not vary. Several studies have suggested that cognitive load, mainly due to gaze concentration towards the road ahead, reduces drivers' ability to utilize explicit roadside instructions (Baumann et al., 2008 ; Engström & Markkula, 2007 ; Muttart et al., 2007). However, there is some evidence that head-up displays (HUDs) lower driver cognitive load and enhance driving performance (Zhu et al., 2021 ; Teng et al., 2023 ). Therefore, to approximate the presentation characteristics of a HUD and maximize sign visibility, the road sign in this study was projected directly into the drivers’ forward field of view (central road area), just before the 2-back task started (Fig. 1 ). Regarding cognitive load manipulation, the baseline drive did not include any secondary task, while the auditory-verbal version of the 2-back task (Mehler et al., 2011 ) was used to induce high levels of cognitive load. Although more ecologically valid tasks, such as hands-free phone conversations, have frequently been used as concurrent cognitive activities in previous driving studies (Horberry et al., 2006 ; McEvoy et al., 2005 ; Strayer & Johnston, 2001 ), the n-back task has emerged as a more controlled and easy-to-measure alternative (e.g., Fotios et al., 2021 ; Fu et al., 2019 ). The 2-back task started 400 meters before the curve entry and finished 400 meters after the curve exit. Participants heard a sequence of numbers (from 0 to 9, presented randomly) and repeated the number presented two numbers before the current one. The auditory stimuli were presented via in-vehicle speakers every 2.25 seconds, and a voice recorder was used to record responses. A schematic representation of the driving scenario is shown in Fig. 2 . Procedure Upon arrival, all participants received verbal instructions about the experiment and, after signing the informed consent, they filled out a brief demographic questionnaire providing information about their age, years of driving, annual mileage, and, in the case of expert drivers, years holding an advanced driving permit. Participants then practiced the 2-back task and, once ready, they moved to the simulator. A 10-minute-long practice drive included segments of driving without any concurrent activity as well as segments where the 2-back task was presented. The experimenter was present during the practice drive to give instructions and answer questions, if required. After the experimenter left the simulator, the main drive started and lasted approximately 20 minutes per participant. Participants were instructed to reach 70 mph in a straight line and to continue driving as they normally would in a real-world setting. They were also instructed to perform the 2-back task as accurately and as quickly as possible. The complete session lasted ∼50 min per participant. Metrics Longitudinal performance was operationalized via mean and minimum speed. Minimum speed was a single value per participant and per road segment, averaged across participants to obtain the mean score. Regarding lateral performance, steering wheel reversal rate (SWRR), steering smoothness, and standard deviation of lane position (SDLP), were calculated. SWRR, a measure of corrective steering, was calculated as the total number of steering reversals greater than 0.1 degrees, divided by the signal’s total length in minutes (Markkula & Engström, 2006 ). SDLP was used as an indicator of variations in drivers’ lane position with respect to the centerline (Verster & Roth, 2011 ). Steering smoothness was calculated as an absolute difference between the raw and smoothed steering wheel angle, with a smaller value indicating better performance (Lehtonen et al., 2014 ). Steering angle signal was smoothed using Sawitzky-Golay filter with 3 seconds moving window. Secondary task performance was quantified as the percentage of missed and incorrect responses in the 2-back task. A response was considered missed if the participant failed to respond within a 2500 millisecond window following stimulus onset. A response was considered an error if the participant responded with an incorrect digit. The total number of misses and errors was summed and divided by the total number of stimuli presented, which served as the performance metric for the cognitive task. Results Since the demand associated with driving the straight and curved segments of the road are different (Fitzpatrick et al., 2000; Serafin, 1994 ), separate data analyses were conducted for the 400-meter-long Approach tangent, and the 432-meter-long Curve sections (Fig. 2 ). A 2 (Driving expertise) × 2 (Road sign) × 2 (Cognitive load) Multivariate Analysis of Variance (MANOVA) was used to analyze lateral (SDLP, SWRR, steering smoothness) and longitudinal (mean and minimum speed) performance, given the multiple dependent variables. A 2 (Driving expertise) × 2 (Road sign) Analysis of Variance (ANOVA) was used for the secondary task performance, as only one outcome measure was included (the percentage of misses). SPSS v26 was used to conduct all the analyses. Type III (Marginal) Sum of Squares was chosen to calculate variances as this type accounts for unequal sample sizes by using harmonic rather than arithmetic mean. Bonferroni correction was applied to counteract the multiple comparisons problem and error bars indicate standard error. All descriptive statistics data tables (Appendix A) as well as those containing the complete set of results for longitudinal (Appendix B), lateral (Appendix C), and secondary task performance (Appendix C) are provided in the supplementary material. Regarding MANOVA assumptions, the Kolmogorov-Smirnov test for the Approach tangent showed significant deflection from normality for 2 minimum speed distributions. Namely, when both the road sign and secondary activity were present, the minimum speed distributions of experts ( p = .01) and non-experts ( p = .004) were negatively skewed. Mean speed, on the other hand, did not show any deflections from normality. Levene’s tests showed that the error variance was unequal across groups in the case of minimum ( p = .04) and mean speed ( p = .05) in the baseline conditions. The assumption of homogeneity of the variance-covariance matrices was not violated. In terms of lateral performance, a deflection from normality was obtained for SDLP in the presence of the sign and secondary activity but only for non-expert drivers ( p = .009). The distribution was positively skewed. Levene’s tests showed that the error variance was equal across groups for all lateral performance indicators and the assumption of homogeneity of the variance-covariance matrices was not violated. For the Curved segment, longitudinal performance indicators did not show any deflections from normality and Levene’s tests showed that the error variance was equal across groups for both indicators. The assumption of homogeneity of the variance-covariance matrices was satisfied. On the other hand, SDLP and SWRR showed significant deflections from normality. SDLP in baseline condition in the case of non-experts ( p < .001), and SWRR in the presence of the sign and secondary activity in the case of experts ( p = .01). All distributions were positively skewed. Levene’s tests showed equal error variance across groups for all lateral performance indicators and the assumption of homogeneity of the variance-covariance matrices was not violated. Given only slight deviations from the assumptions, MANOVA, known to be relatively robust to violations of normality, was considered appropriate for analyzing the data, and Wilks’ lambda (Wilks’ Λ ) was utilized as an appropriate test statistic. In terms of secondary task performance, the Kolmogorov-Smirnov test did not show any significant deflection from normality for any of the two road segments. Levene’s tests showed that the error variance was equal across groups and the assumption of homogeneity of the variance-covariance matrices was satisfied. Given these results, ANOVA was deemed appropriate for the analysis. The effects of cognitive load on expert and non-expert drivers' longitudinal and lateral performance Approach tangent Mixed-model MANOVA did not show any significant effects of Cognitive load on longitudinal performance of Expert and Non-expert drivers at the Approach tangent. The presence of the 2-back task did not affect drivers’ minimum or mean speed, compared to No Task condition. In terms of lateral performance, mixed-model MANOVA showed a significant main effect of Cognitive load (Wilks’ Λ = .452, F (3,30) = 12.11, p < .001, partial η 2 = .55) for the Approach tangent. Univariate analysis showed that the main effect of Cognitive load was significant for SDLP ( p = .007, partial η 2 = .21), 0.1° SWRR ( p < .001, partial η 2 = .33), and steering smoothness ( p = .05, partial η 2 = .11). Lane deviations decreased ( M = .41, SE = .02), small steering corrections increased ( M = 61.70, SE = 3.82), and steering smoothness decreased ( M = .38, SE = .02) in the 2-back condition, compared to No Task condition ( M SDLP = .46, SE = .03; M SWRR = 49.15, SE = 3.08; M Smooth = .32, SE = .02), irrespective of Driving expertise. Curved segment Mixed-model MANOVA revealed no significant effects of Cognitive load on longitudinal performance of Expert and Non-expert drivers during the Curved segment. The inclusion of the 2-back task did not influence drivers’ minimum or mean speed compared to the No Task condition. When a mixed-model MANOVA was run for the lateral performance indicators, the main effect of Cognitive load (Wilks’ Λ = .441, F (3,30) = 12.65, p < .001, partial η 2 = .56) was found significant. Further analyses showed that the main effect of Cognitive load was significant for 0.1-degree SWRR ( p < .001, partial η 2 = .47), and almost reached significance in the case of steering smoothness ( p = .06). As Fig. 3 shows, in Curved segments, the number of small steering corrections increased in the 2-back ( M = 68.24, SE = 3.29) compared to No Task conditions ( M = 58.84, SE = 3.18), irrespective of Driving expertise. The effects of road sign on expert and non-expert drivers' longitudinal and lateral performance Approach tangent When a mixed-model MANOVA was run for the longitudinal performance at the Approach tangent, the main effects of Driving expertise (Wilks’ Λ = .810, F (2,31) = 3.31, p = .05, partial η 2 = .17), and Road sign (Wilks’ Λ = .711, F (2,31) = 6.31, p = .005, partial η 2 = .29) were found to be statistically significant. Moreover, a significant interaction effect was seen between Road sign and Expertise (Wilks’ Λ = .792, F (2,31) = 4.06, p = .03, partial η 2 = .21), suggesting that the effects of Road sign on longitudinal performance were different for Expert and Non-expert drivers. Univariate analyses showed that, compared to the No Sign condition ( M = 63.63, SE = 0.85), presence of the Sign ( M = 61.24, SE = 1.04) significantly decreased drivers’ minimum speed ( p = .002, partial η 2 = .27). Additionally, while approaching the curve, Expert drivers’ minimum speed ( M = 63.64, SE = 1.35) was significantly higher ( p = .05, partial η 2 = .16) when compared to that seen for Non-experts ( M = 61.23, SE = 1.13). On the other hand, the Road sign by Expertise interaction effect was significant only for mean speed ( p < .01, partial η 2 = .18). As shown in Fig. 4 , mean approach speed was lower in the Sign, compared to No Sign condition, but only for Expert drivers. While the mean speed of Experts was higher than the mean speed of Non-experts when there was No Sign, differences in mean speed between these two groups were not found in the Sign condition. In terms of lateral performance, no main or interaction effects of Road sign and Driving expertise were found statistically significant for the Approach tangent. Curved segment Mixed-model MANOVA performed on the two longitudinal performance indicators showed a significant main effect of Road sign (Wilks’ Λ = .698, F (2,31) = 6.70, p = .004, partial η 2 = .30) and a significant main effect of Expertise (Wilks’ Λ = .770, F (2,31) = 3.72, p = .05, partial η 2 = .14). No other main or interaction effects were obtained. Univariate analyses found that the main effect of Road sign was significant for the minimum ( p < .001, partial η 2 = .30) and mean speed ( p = .006, partial η 2 = .22). In both cases, the speed in Curved segments decreased in the Sign ( M mean = 55.78, SE = 1.11; M min = 51.60, SE = 1.22), compared to No Sign condition ( M mean = 57.19, SE = 0.96; M min = 53.34, SE = 1.07), irrespective of Driving expertise. Similarly, the main effect of Driving expertise was found significant for the minimum ( p = .05, partial η 2 = .11) and mean speed ( p = .04, partial η 2 = .12). Expert drivers negotiated curves with higher minimum ( M = 54.69, SE = 1.73) and mean speed ( M = 58.56, SE = 1.54) compared to Non-experts ( M min = 50.34, SE = 1.45; M mean = 54.33, SE = 1.29), irrespective of Road sign. Since the same pattern of changes was obtained for both speed indicators, Fig. 5 shows the main effects of Expertise and Road sign on minimum speed only. In the case of lateral performance in Curved segments, MANOVA yielded a significant main effect of Road sign (Wilks’ Λ = .712, F (3,30) = 2.71, p < .05, partial η 2 = .19). Following further analysis, the main effect of Road sign was significant for SDLP ( p = .01, partial η 2 = .18) and steering smoothness ( p = .05, partial η 2 = .10). Displayed in Fig. 6 , when the Sign was present, lane deviations in Curves decreased ( M = .431, SE = .02) and steering smoothness increased ( M = .736, SE = .07) compared to No Sign drives ( M SDLP = 496, SE = .03; M Smooth = .890, SE = .08). The effects of road sign on expert and non-expert drivers' longitudinal and lateral performance with and without the 2-back task Approach tangent In terms of longitudinal performance, a mixed-model MANOVA did not show any significant interaction effects between Cognitive load and Road sign at the Approach tangent. On the other hand, a significant Cognitive load x Road sign interaction effect (Wilks’ Λ = .781, F (3,30) = 2.94, p = .05, partial η 2 = .21) on lateral performance was obtained for the Approach tangent. The Cognitive load by Road sign interaction effect was found to be significant only for SDLP ( p = .05, partial η 2 = .10). As displayed in Fig. 7 , compared to the No Task condition, the 2-back decreased SDLP only when the Sign was not present. In the absence of the 2-back task, the Sign decreased SDLP, compared to No Sign condition. However, differences between the two Road sign conditions were not obtained when the 2-back task was introduced. Curved segment For both longitudinal and lateral performance indicators, mixed-model MANOVAs did not reveal any significant interaction effects between Cognitive load and Road sign at the Curved segment. The effects of driving expertise and road sign on secondary task performance A 2 (Experts, Non-experts) x 2 (No Sign, Sign) mixed-model ANOVA on the percentage of misses was run separately for the two road segments. For both the Approach tangent and Curved segment, the Kolmogorov-Smirnov test did not show any significant deflection from normality. Levene’s tests showed that the error variance was equal across groups and the assumption of homogeneity of the variance-covariance matrices was satisfied. Given these results, ANOVA was deemed appropriate for the analysis. Approach tangent No statistically significant main or interaction effects were found when the 2x2 ANOVA was performed on the percentage of misses. This indicates that Experts performed the same as Non-experts in the 2-back task, and the presence of the Sign did not affect their performance. Curved segment A 2x2 ANOVA for the percentage of misses while negotiating curves showed a significant Road sign x Expertise interaction effect (Wilks’ Λ = .762, F (1,32) = 3.52, p = .04, partial η 2 = .13). As shown in Fig. 8 , the percentage of misses did not differ between Experts and Non-experts in the No Sign condition. Only Expert drivers’ percentage of misses decreased when the Sign was shown. Due to this, Expert drivers had a lower percentage of misses compared to Non-experts in the presence of the Sign. Discussion This driving simulator study explored if the use of a road sign that provides a preview of an upcoming curve alters longitudinal and lateral performance of cognitively loaded expert and non-expert drivers, while approaching and negotiating curves. Based on previous findings (Liao et al., 2018 ; Muttart et al., 2013 ), expert drivers were expected to decrease their speed more than non-experts, when engaged in a secondary task. Although expert drivers maintained a higher minimum speed at the approach tangent, and higher mean speed at both the approach and curved segments, we found no additional changes in speed during the secondary task conditions. Research has shown that, with experience, drivers automatically maintain an optimal speed, tuned to the road and driving conditions (Engström et al., 2017 ; Lewis-Evans et al., 2011 ; Recarte & Nunes, 2002 ). Deviations from this optimal speed require cognitive resources, and if these resources are being used by another, secondary activity, drivers tend to resort to their optimal speed (Engström et al., 2017 ). Therefore, it seems that both groups already drove at their optimal speed, allowing them to perform the 2-back task equally well without the need for any speed adjustments. Conversely, in the presence of the road sign, both groups reduced their minimum speed in the curve approach, and both minimum and mean speed were reduced while negotiating the curve. This supports several earlier studies which have shown that road signs can prime drivers ahead of curves with a limited sight distance (Charlton, 2004 ; Crundall & Underwood, 2001 ; Koyuncu & Amado, 2008 ). Charlton ( 2004 ), for instance, showed that road signs similar to the one used in this study successfully reduced the speed of cognitively loaded drivers, when they approached and negotiated tight curves at 45 kph (≈ 28 mph). However, this was not always seen for wider curves, driven at 65 kph (≈ 40 mph) and 85 kph (≈ 53 mph). Although speed is thought to diminish the positive effects of road signs (Koyuncu & Amado, 2008 ), the present study demonstrates that road signs can still prompt a speed reduction even at higher speeds (70 mph), provided they are presented with sufficient lead time. In this case, the distance available before entering the curve was sufficient for drivers to process the information and initiate a deceleration response. Moreover, the road sign used in this study reduced the driving speed of both groups, irrespective of cognitive load. The road sign was expected to decrease expert drivers’ speed more than non-experts’, in both road segments. However, this difference was only seen for the approach tangent. This anticipatory behavior was expected by highly skilled drivers, due to their more accurate mental representation of the upcoming curve, triggered by the road sign (Crundall et al., 2003 ; Stahl et al., 2019 ). Further support for this explanation comes from the secondary task performance results, indicating that, when prompted by a sign ad driving through the curve, expert drivers’ misses decreased, and they also missed less responses to the n-back compared to non-experts. On the other hand, previous research has shown that less skilled drivers often adopt a reactive rather than proactive driving style (Crundall & Underwood, 2001 ), which could explain late reaction to the road sign of non-expert drivers, who reduced their speed only in curves. However, the modest differences in minimum and mean speed between expert and non-expert drivers during the approach and curved segments (ranging from 2 to 5 mph), along with a difference in missed n-back responses in curves of around 12%, suggest that distinctions between these two groups are relatively subtle, particularly when compared to the more pronounced differences typically reported between novices and experienced drivers (e.g., Crundall & Underwood, 2001 ). In terms of lateral performance, the commonly reported increase in small steering corrections and decrease in deviations from the centerline was observed when drivers were engaged in a cognitive secondary activity (Engström et al., 2005 ; Kountouriotis et al., 2015 ; Li et al., 2018 ), but only for the approach segment. Based on the active gaze hypothesis, which suggests that drivers tend to steer in the direction of their gaze (Robertshaw & Wilkie, 2008 ; Wilkie et al., 2008 ; Wilkie et al., 2010 ), increased micro-steering adjustments and a consequent decrease in deviations from the centerline are linked to the increased concentration of gaze towards the road center, for cognitively loaded drivers (Kountouriotis & Merat, 2016 ; Kountouriotis et al., 2015 ; Li et al., 2018 ; Wang et al., 2014 ). As such a relationship between steering corrections and lane deviations was not found for the curved segments, with the 2-back condition only increasing the number of steering corrections, our initial hypothesis was only partially confirmed. Previous studies have shown that when negotiating tight curves, drivers typically maintain a more central position than required, as this allows them to have a good peripheral view of both road edges (Land & Horwood, 1995 ; Robertshaw & Wilkie, 2008 ). In this study, both groups of drivers already drove quite close to the centerline, which may explain the absence of changes in lane deviations in the curved segments due to cognitive load. Cognitive load decreased steering smoothness of both expert and non-expert drivers at the approach tangent. This might imply that the gaze concentration effect influences the collection of far-road information used by drivers (and especially experts, see: Crundall et al., 2003 ; Land & Tatler, 2001 ; Lappi et al., 2017 ; van Leeuwen et al., 2017 ) to preview upcoming changes in direction in order to maintain smooth steering (Boer, 2016 ; Donges, 1978 ; Okafuji et al., 2018 ; Salvucci & Gray, 2004 ). As with lane deviations, a decrease in steering smoothness was not obtained for the curved segment, suggesting that further experimentation is needed to understand how lateral position is maintained in curves, particularly when drivers are cognitively loaded. In support of the initial hypothesis, the road sign did not affect steering corrections for any road segment but increased steering smoothness for the curved segment. Whereas steering smoothness relies on far-road information, steering corrections are believed to be based on information obtained from the near-road region (Salvucci & Gray, 2004 ). The purpose of the sign was to facilitate the prediction of the trajectory in the timeframe of a few seconds, thus facilitating the perception of far-road information and increasing steering smoothness, as seen in this study. Moreover, when drivers were negotiating curves, the sign increased their steering smoothness and decreased lane deviations, irrespective of cognitive load level. Supporting earlier findings (Charlton, 2004 ; Crundall & Underwood, 2001 ; Koyuncu & Amado, 2008 ), this can be the advantage of such road signs, assisting drivers even when their resources are taxed by another cognitive activity. From an applied perspective, curve preview cues appear to facilitate path planning by activating representations of upcoming road geometry, even under cognitive load. This supports “just-in-time” information delivery approaches, where early activation of mental models guides attention. Integrating these principles into HUDs and driver assistance systems may aid cognitive offloading in demanding situations (e.g., low-visibility curves), with potential safety benefits. Although more skilled drivers, who are generally faster at identifying road signs and whose responses are often automatized (Babić et al., 2019 ; Borowsky et al., 2008 , Koyuncu & Amado, 2008 ), are expected to benefit more than less skilled drivers when road scenes are primed by road signs, this study did not show many differences in performance between expert and non-expert (but experienced) drivers, irrespective of cognitive load. It should be noted, however, that a larger sample size would likely have increased the statistical power of the analyses, improving the ability to detect subtle but potentially meaningful differences between groups. Moreover, the findings suggest that the relationship between skill automatization and driving experience or expertise may not be strictly linear. It is possible that there is a threshold in the development of automaticity beyond which further driving experience or training does not substantially enhance automatic performance. This could at least partially explain the limited differences observed in speed and steering control between expert and non-expert, but experienced drivers. Importantly, these findings also challenge prevailing theoretical perspectives on driving expertise, which posit that driving expertise, typically acquired through deliberate and structured training, is qualitatively distinct from mere driving experience (Ericsson et al., 1993 ). In contrast, the present results suggest that driving expertise may not be entirely domain-general, but rather task-specific. That is, the advantages of highly trained drivers (such as advanced police drivers included in this sample) may be most evident in specific driving tasks, such as hazard perception, anticipatory scanning, or high-speed pursuit and emergency response driving, rather than in more general measures of vehicle control under standard conditions. Future studies could benefit from more precisely matching the experience levels (e.g., years of driving or mileage) between expert and non-expert groups, but also from including less experienced drivers to assess the effectiveness of road signs. Additionally, future research could leverage gaze patterns, which are well-established indicators of increased cognitive load (Lehtonen et al., 2013 ) and are particularly informative when considering driving experience (Lehtonen et al., 2014 ; Liao et al., 2018 ; Underwood et al., 2002 ) or expertise (Pammer & Blink, 2018 ; Pammer et al., 2018 ). Namely, it is still unclear how cognitive load affects experts’ gaze patterns and whether such changes can be directly associated with their driving performance. The inclusion of only one type of road sign and only one type of curve is a limitation of this study, as there is evidence that the perception and effectiveness of signs depend on the road context (Vilchez, 2015 ; Vilchez, 2019). Road signs are thought to enhance driving performance in environments lacking sufficient cues, such as curves with limited sight distance (Crundall & Underwood, 2001 ). It is, therefore, possible that the road sign used in this study provided information that was redundant to cues already available in the environment, which may have diminished observable differences in performance. Future studies could benefit from incorporating less predictable curves to explore whether such road signs act as cues which effectively elicit automatized behaviors, particularly under cognitive load. Moreover, systematic variations of both road geometry, width, environmental visual cues, and sign characteristics (e.g., symbolic versus textual signs, early versus late placement) might provide valuable insights into how different sign-curve combinations influence driver behavior under cognitive load. Such work would help clarify the boundary conditions under which road signs function most effectively and support the practical application of these findings across varied real-world driving contexts. While the n-back task remains a widely used and controllable method for inducing cognitive load in experimental settings (Mehler et al., 2011 ), its ecological validity may be limited when compared to more naturalistic cognitive distractions, such as hands-free phone conversations or interactions with passengers. These real-world situations often involve social, emotional, or unpredictable elements that are not fully captured by the structured and repetitive nature of the n-back task. Future research might, therefore, incorporate more realistic secondary tasks to assess whether the effects observed here generalize to such real-world driving scenarios. Conclusion While researchers have theorized that cognitive load disproportionately affects the performance of drivers of different skill levels (e.g., Engström et al., 2017 ; Fuller, 2005 ), there is limited empirical evidence to support such a notion. This study explored the combined effects of cognitive load and a road sign showing the preview of an upcoming curve on the performance of expert and non-expert drivers. Expert drivers showed superior longitudinal performance compared to non-experts in the absence of cognitive load. However, neither group’s speed was reduced by engagement in the cognitively loading task. Regarding lateral performance, both groups showed compensatory behaviors when driving and performing the 2-back task, including increased small steering adjustments, reduced steering smoothness, and decreased deviations from the centerline, mostly obtained at the curve approach tangent. The absence of significant lateral performance differences in the curved segment, despite changes in steering corrections and smoothness at the approach tangent, underscores the need for further investigation regarding how drivers maintain lateral position under cognitive load. The road sign proved effective in prompting speed reductions and enhancing steering smoothness, suggesting its utility even in cognitively demanding situations. Moreover, the road sign led to improved 2-back task performance of expert drivers, when negotiating curves. However, the nuanced differences between expert and non-expert drivers, particularly in speed adjustments and secondary task performance when exposed to the road sign underline the need to consider driving expertise as task-specific rather than purely domain-general. These findings highlight the importance of expanding the sample to include a greater number of expert drivers, while also incorporating less experienced drivers, examining performance across more targeted tasks, such as hazard perception and anticipatory scanning, that may be more sensitive to differences in expertise. While the study demonstrated the effectiveness of road signs in enhancing driving performance, the inclusion of only one road sign and one curve type limits its generalizability. Future research should explore a broader range of curve geometries and incorporate more unpredictable road environments to better understand the role of road signs as effective cues for automatic vehicle responses. The use of more ecologically valid secondary tasks would provide deeper insights into how skill level and cognitive load interact to influence driving performance. Gaze data can further illuminate the relationship between cognitive load, driving expertise, and driving performance, offering practical implications for road design and driver training programs. Declarations Funding and Competing interests The authors have no competing interests to declare that are relevant to the content of this article. No funding was received to assist with the preparation of this manuscript and the authors declare they have no financial interests. Author Contribution MC: Conceptualization, Methodology, Investigation, Formal analysis, Visualization, Writing – Original draft preparation. JB: Supervision, Project administration, Writing – Reviewing and Editing. NM: Supervision, Project administration, Writing – Reviewing and Editing. Acknowledgement This study was conducted as part of a collaborative PhD project between Rimac Technology LLC and the Institute for Transport Studies at the University of Leeds. The authors would like to thank Michael Daly, Anthony Horrobin, and Albert Solernou Crusat for creating experimental scenarios and supporting data collection. Data Availability The dataset used in this study is not publicly available, as it is owned by Rimac Technology LLC. However, it may be made available upon reasonable request and with permission from Rimac Technology LLC. References Babić D, Tremski Š, Babić D (2019) Investigation of traffic signs understanding – Eye tracking case study. Tehnički Vjesn 26(1):29–35 Baumann MRK, Petzoldt T, Groenwoud C, Hogema J, Krems J (2008) The effect of cognitive tasks on predicting events in traffic. 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Springer International Publishing. https://doi.org/10.1007/978-3-030-78227-6_20 Tables Appendix A. Table 1 Descriptive statistics for longitudinal performance indicators for the Approach tangent and Curved segment Driving expertise Cognitive load Road sign Minimum speed (mph) Mean speed (mph) N M SD Range M SD Range Approach tangent Expert drivers 2-back task Sign 20 61.90 7.62 48.79–68.92 67.58 2.39 60.14–70.01 No sign 20 65.47 4.36 55.17–69.38 68.59 1.60 60.48–69.96 No task Sign 20 62.53 5.71 52.85–67.07 67.46 2.47 57.54–69.65 No sign 20 64.63 4.28 56.29–68.17 68.37 1.47 62.83–69.89 Non-expert drivers 2-back task Sign 20 61.09 9.16 31.45–68.58 68.55 2.70 59.79–69.91 No sign 20 62.06 6.92 32.67–69.14 67.64 2.21 58.42–69.31 No task Sign 20 59.43 7.42 40.53–69.31 67.65 1.90 56.29–69.94 No sign 20 62.34 7.12 36.34–69.33 67.67 2.09 58.41–69.58 Curved segment Expert drivers 2-back task Sign 14 53.38 6.77 43.74–65.17 57.50 5.21 47.31–67.39 No sign 14 56.40 6.26 47.44–68.02 59.95 4.81 53.68–69.54 No task Sign 14 54.13 5.34 44.69–62.52 58.11 4.30 50.21–65.25 No sign 14 54.81 5.45 48.32–62.74 58.65 3.82 52.59–65.33 Non-expert drivers 2-back task Sign 14 50.11 8.44 36.58–69.10 54.05 7.90 38.17–68.15 No sign 14 51.90 7.33 35.67–65.87 55.55 7.46 43.60–68.79 No task Sign 14 48.89 8.07 38.31–66.32 53.24 7.09 44.78–68.02 No sign 14 50.46 7.20 33.15–66.63 54.66 7.78 38.75–68.15 Table 2 Descriptive statistics for lateral performance indicators for the Approach tangent and Curved segment Driving expertise Cognitive load Road sign SDLP (m) 0.1° SWRR (deg) Steering smoothness (deg) N M SD Range M SD Range M SD Range Approach tangent Expert drivers 2-back task Sign 20 0.45 0.16 0.23–0.76 65.44 25.69 29.48–118.92 0.42 0.15 0.23–0.74 No sign 20 0.45 0.14 0.23–0.66 65.96 23.07 29.54–122.67 0.37 0.16 0.14–0.81 No task Sign 20 0.45 0.11 0.24–0.61 53.44 20.45 25.46–78.75 0.34 0.13 0.14–0.60 No sign 20 0.57 0.17 0.22–0.88 54.07 21.25 21.52–91.02 0.35 0.14 0.18–0.63 Non-expert drivers 2-back task Sign 20 0.36 0.16 0.15–0.89 54.51 24.32 18.32–110.89 0.31 0.11 0.15–0.54 No sign 20 0.36 0.16 0.18–0.85 60.88 20.23 39.64–101.88 0.32 0.12 0.18–0.59 No task Sign 20 0.41 0.17 0.13–0.79 44.89 17.62 10.78–76.60 0.27 0.1 0.13–0.54 No sign 20 0.42 0.19 0.08–1.03 44.19 17.11 18.44–81.92 0.31 0.17 0.11–1.00 Curved segment Expert drivers 2-back task Sign 14 0.46 0.17 0.19–0.90 71.1 20.75 50.91–122.08 0.9 0.15 0.27–2.38 No sign 14 0.51 0.17 0.29–0.81 72.95 18.85 44.38–108.45 1.02 0.23 0.36–2.49 No task Sign 14 0.46 0.17 0.20–0.79 60.41 19.56 30.76–95.59 0.62 0.19 0.20–1.78 No sign 14 0.55 0.17 0.26–0.86 58.48 18.05 43.00–103.34 0.84 0.21 0.22–2.44 Non-expert drivers 2-back task Sign 14 0.41 0.13 0.23–0.71 67.02 23.72 39.42–140.74 0.82 0.15 0.23–2.89 No sign 14 0.42 0.19 0.16–0.97 61.89 15.63 41.20–107.86 0.84 0.16 0.30–2.15 No task Sign 14 0.39 0.13 0.24–0.73 57.38 20.28 28.21–111.08 0.61 0.15 0.24–2.09 No sign 14 0.5 0.2 0.26–1.13 59.09 18.61 35.28–96.53 0.86 0.17 0.26–1.70 Appendix B. Table 3 The results of univariate tests for longitudinal performance indicators for the Approach tangent and Curved segment Approach tangent Curved segment Min speed F (df) p η2 π F (df) p η2 π Expertise 2.86 (1,32) .05 .16 .69 3.71 (1,32) .05 .11 .46 Road sign (RS) 11.54 (1,32) .002 .27 .91 13.8 (1,32) < .001 .30 .95 Cognitive load (CL) .19 (1,32) .67 .006 .07 1.44 (1,32) .24 .04 .21 Expertise x RS .40 (1,32) .53 .01 .10 .03 (1,32) .86 .001 .05 Expertise x CL .10 (1,32) .75 .003 .06 .39 (1,32) .54 .01 .09 Road sign x CL .04 (1,32) .84 .001 .05 1.08 (1,32) .31 .03 .17 Expertise x CL x RS 2.25 (1,32) .14 .07 .31 .75 (1,32) .40 .02 .13 Mean speed Expertise .05 (1,32) .83 .001 .06 4.31 (1,32) .04 .12 .52 RS .97 (1,32) .33 .03 .16 8.85 (1,32) .006 .22 .82 CL 1.11 (1,32) .30 .03 .18 .72 (1,32) .40 .02 .13 Expertise x RS 7.12 (1,32) .01 .18 .74 .001 (1,32) .97 .00 .05 Expertise x CL .20 (1,32) .66 .006 .07 .13 (1,32) .72 .004 .06 Road sign x CL .46 (1,32) .50 .01 .10 .95 (1,32) .34 .03 .16 Expertise x CL x RS .75 (1,32) .39 .02 .13 .81 (1,32) .38 .03 .14 Note . η2 = partial eta square, π = statistical power. Appendix C. Table 4 The results of univariate tests for lateral and secondary task performance indicators for the Approach tangent and Curved segment Approach tangent Curved segment SDLP F (df) p η2 π F (df) p η2 π Expertise 3.69 (1,32) .06 .10 .44 2.7 (1,32) .11 .08 .36 Road sign (RS) 2.33 (1,32) .14 .07 .32 6.72 (1,32) .01 .17 .71 Cognitive load (CL) 8.33 (1,32) .007 .21 .78 .96 (1,32) .33 .03 .16 Expertise x RS 1.37 (1,32) .25 .04 .21 .06 (1,32) .82 .002 .06 Expertise x CL .16 (1,32) .69 .005 .07 .03 (1,32) .87 .001 .05 Road sign x CL 3.97 (1,32) .05 .16 .49 1.12 (1,32) .29 .03 .18 Expertise x CL x RS 1.73 (1,32) .20 .05 .25 .19 (1,32) .66 .006 .07 0.1° SWRR Expertise 1.94 (1,32) .17 .06 .27 .50 (1,32) .49 .02 .11 RS .94 (1,32) .34 .03 .16 .31 (1,32) .58 .01 .08 CL 15.61 (1,32) < .001 .33 .97 27.86 (1,32) < .001 .47 .99 Expertise x RS .41 (1,32) .53 .01 .09 .28 (1,32) .60 .009 .08 Expertise x CL .04 (1,32) .85 .001 .05 3.19 (1,32) .08 .09 .41 Road sign x CL .70 (1,32) .41 .02 .13 .26 (1,32) .62 .008 .08 Expertise x CL x RS .74 (1,32) .39 .02 .13 3.12 (1,32) .08 .09 .40 Steering smoothness Expertise 3.08 (1,32) .08 .09 .40 .66 (1,32) .006 .20 .07 RS .03 (1,32) .87 .001 .05 3.84 (1,32) .05 .14 .45 CL 3.88 (1,32) .05 .15 .45 3.56 (1,32) .06 .10 .38 Expertise x RS 4.2 (1,32) .05 .17 .51 .04 (1,32) .83 .001 .06 Expertise x CL .57 (1,32) .45 .02 .11 .56 (1,32) .46 .02 .11 Road sign x CL 1.93 (1,32) .17 .06 .27 1.12 (1,32) .30 .03 .18 Expertise x CL x RS .25 (1,32) .62 .008 .08 .14 (1,32) .71 .004 .06 Percentage of misses Expertise .37 (1,32) .55 .01 .09 .50 (1,32) .48 .02 .11 Road sign .19 (1,32) .66 .006 .07 .67 (1,32) .45 .02 .11 Expertise x RS .007 (1,32) .93 .00 .05 3.52 (1,32) .04 .13 .35 Note. η2 = partial eta square, π = statistical power. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 29 Apr, 2026 Submission checks completed at journal 29 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9449373","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634428928,"identity":"980f9665-65a2-48a9-932b-a807ebf209c2","order_by":0,"name":"M. Celic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYLCCDwVA4gCEbUBQNQ8QM84wIFULMw9JWuz5Tyd+tjGwkeM7f4Dxww+Gw8aEbZHI3SydY5BmLHkjgVmyh+GwGRFaeLcx5xgcTtxwg4FBmoHhsA1hLfxntzFbGPyv33D+APNv4rQw5G5jZjA4kABEbCBbiHDYjdzNkj0GyYYzbyS2WfYYpBP2Pnv/2Y0fflTYyfOdP3z4xo8Ka8MGgnoQgLGBmFgZBaNgFIyCUUAMAACyqzbuwT77vwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Leeds","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"","lastName":"Celic","suffix":""},{"id":634428929,"identity":"d83e3dda-a948-40a6-8f13-95ffa693b431","order_by":1,"name":"J. Billington","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"","lastName":"Billington","suffix":""},{"id":634428930,"identity":"0718052a-204f-4aed-a62a-d395a30d6e3f","order_by":2,"name":"N. Merat","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"","lastName":"Merat","suffix":""}],"badges":[],"createdAt":"2026-04-17 12:55:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9449373/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9449373/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108978618,"identity":"5ef28004-dca9-4607-94d9-fbf00bd079fd","added_by":"auto","created_at":"2026-05-11 11:46:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1200514,"visible":true,"origin":"","legend":"\u003cp\u003eDriving environment and the sign for the right curve direction\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/b7f10bd6c22fc6a72c4161d1.png"},{"id":108978617,"identity":"1b8e5a2a-db80-4b9a-b829-a7dfef4d65a6","added_by":"auto","created_at":"2026-05-11 11:46:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":578930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA schematic representation of the driving scenario (right curve)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/264c961eaec4c34f2b97b7cb.png"},{"id":108980877,"identity":"e1d4c912-438e-479b-bc63-49691706444b","added_by":"auto","created_at":"2026-05-11 12:12:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23821,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe main effect of Cognitive load on 0.1\u003c/em\u003e°\u003cem\u003e SWRR for Curved segment\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/332ebac3e65dc2dfba0c6c94.png"},{"id":108979665,"identity":"ab177661-d89c-4f77-bd46-a5a7ae7e9174","added_by":"auto","created_at":"2026-05-11 12:00:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33079,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Road sign x Expertise interaction effect on mean speed for the Approach tangent\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/a629f8c293b20cefe9ddedf2.png"},{"id":108978621,"identity":"1750a688-efbb-4df9-bf06-debe35df7635","added_by":"auto","created_at":"2026-05-11 11:46:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33678,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe main effects of Road sign and Expertise on minimum speed for Curved segment\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/025e3fdfa53a75471167618b.png"},{"id":108978625,"identity":"448cdba2-3589-4383-8c4d-fc3e91fa31d7","added_by":"auto","created_at":"2026-05-11 11:47:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":22553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe main effect of Road sign on SDLP and steering smoothness for Curved segment\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/03985d7a1958748e2f2612b2.png"},{"id":108978634,"identity":"75307636-0ddf-4013-8552-0003952768fd","added_by":"auto","created_at":"2026-05-11 11:47:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":26631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Cognitive Load x Road sign interaction effect on SDLP for the Approach tangent\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/631dccdd7e06e059f9c560fb.png"},{"id":108979589,"identity":"1a88a4c4-0722-4fc9-b560-b31e56b3ef44","added_by":"auto","created_at":"2026-05-11 12:00:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":25171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Road sign x Expertise on the percentage of misses for Curved segment\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/f9e28cb7c71d3c9c23aba4fe.png"},{"id":108983348,"identity":"6b3e7c56-e2dd-4618-bcf6-8dacabecea55","added_by":"auto","created_at":"2026-05-11 12:33:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2777708,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9449373/v1/675bb959-61b6-43af-a661-c8ec8601b20d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Can a preview of an upcoming curve mitigate the effects of cognitive load on expert and non-expert drivers’ vehicle control?","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Expert drivers reach higher minimum and mean speed than non-expert drivers.\u003c/p\u003e\u003cp\u003e\u0026bull; Cognitive load does not affect experts\u0026rsquo; and non-experts\u0026rsquo; driving speed.\u003c/p\u003e\u003cp\u003e\u0026bull; Cognitive load reduces steering smoothness and increases small steering corrections.\u003c/p\u003e\u003cp\u003e\u0026bull; Cognitive load decreases lane deviations only in the absence of curve preview.\u003c/p\u003e\u003cp\u003e\u0026bull; Curve preview reduces driving speed and lane deviations and increases steering smoothness.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eIt is well established that environmental and road-related factors, such as limited visibility or sharp road curvature, increase driving demands, especially when combined with high travel speeds (Fuller, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lappi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or inadequate driver skills (Lehtonen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The pervasive integration of modern in-vehicle technologies adds to these demands by occupying drivers\u0026rsquo; cognitive resources and increasing their cognitive load. While driving skill is critical for safe vehicle control (Beanland \u0026amp; Wynne, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Clarke et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lappi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pammer \u0026amp; Blink, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pammer et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), a lack of theoretical and empirical coherence in this context makes it difficult to ascertain how skill interacts with non-driving-related cognitive distractions to influence performance. Some researchers attribute better performance in skilled drivers to experience-based development of perceptual and cognitive skills (e.g., Drummond, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Harrison, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Sivak, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). However, findings from studies on expert drivers, such as advanced police drivers, driving instructors, or racing drivers, suggest that such driving performance requires more than experience alone (Crundall et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Crundall et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Johnston \u0026amp; Scialfa, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Land \u0026amp; Tatler, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lappi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pammer \u0026amp; Blink, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although skill automatization has been proposed as a key mechanism for coping with demanding traffic situations (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fuller, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), driving does not rely solely on either automatic or controlled processing. Rather, it reflects a dynamic interplay between the two, with their relative contribution shifting based on both driver skill level and situational demands (Schneider \u0026amp; Shiffrin, 1977; Fisk \u0026amp; Schneider, 1984). While expert drivers are generally expected to possess more automatized skills, typically developed through extensive driving experience and deliberate training (Ericsson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Pammer et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), it remains unclear whether this advantage consistently buffers against the added strain of concurrent cognitive tasks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1 Cognitive load, driving expertise, and curve negotiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost theoretical models of human information processing and task performance concur that humans have limited processing capacity which, when exceeded by task demands, leads to performance decrements (e.g., Broadbent, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1958\u003c/span\u003e; Kahneman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Wickens, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). This is particularly evident when cognitive resources are divided across two or more simultaneous tasks, with the level of resources required to perform such concurrent activities being referred to as cognitive load (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Engstr\u0026ouml;m and colleagues (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), for a task such as driving, cognitive load primarily affects activities that require cognitive control and resources, while leaving resource-free automatized skills largely unaffected. The degree of skill automatization, acquired through repeated experience with a task, leads to identical driving situations imposing varying demands on novice, experienced, and expert drivers. Drivers with more automatized skills are, therefore, believed to demonstrate superior performance even under cognitive load, compared to their less skilled counterparts (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, cognitively loaded experienced drivers, when compared to less experienced/novice drivers, tend to resort to their automatically controlled optimal speed, effectively compensating for increased task demands (Liao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, advanced paramedic drivers exhibit the lowest crash rates, despite driving at high speeds while managing various visual and cognitive distractions (Symmons et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn terms of lateral vehicle control, our previous study (Celic et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that expert racing drivers adhered more closely to the optimal driving line, while experienced drivers deviated more when cognitively loaded. These findings align with research suggesting that drivers tend to steer where they look (e.g., Wilkie et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and that cognitive load narrows gaze toward the road ahead, leading to more frequent micro-steering corrections and reduced lane deviations (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lehtonen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Reimer, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While such effects are typically observed in non-expert drivers, experts, who rely on peripheral vision to gather near-road information for immediate steering control without expending cognitive resources (Land \u0026amp; Horwood, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Summala et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), may not exhibit an increase in small steering corrections under cognitive load. This strategy allows them to allocate more resources to trajectory planning, spending more time looking far ahead (Crundall et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Konstantopoulos et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Land \u0026amp; Tatler, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lappi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Muttart et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tuhkanen et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; van Leeuwen et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and adjusting speed and steering in alignment with anticipated road curvature (He \u0026amp; Donmez, 2020; Stahl et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, although the visual narrowing caused by cognitive load might not disrupt expert drivers\u0026rsquo; use of near-road information for steering corrections, it might interfere with their ability to sample and process far-road information, potentially leading to more abrupt and less smooth steering. Yet, given the scarcity of studies specifically addressing how cognitive load affects expert drivers\u0026rsquo; performance, further research is needed to clarify these effects.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Use of road signs as directional cues in curve negotiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eRoad signs serve as critical environmental cues, helping drivers anticipate curves and adjust their speed, especially on approach to curves with limited sight distance (Campbell et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Charlton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Costa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Vos et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies using a priming paradigm have consistently shown that even brief exposures to traffic signs can automatically prompt faster reactions to subsequent road scenes and facilitate curve anticipation (Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, their efficacy appears to depend on the type of road sign (Charlton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Charlton, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), its location (Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), as well as the driving speed (Charlton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWhile experienced drivers benefit more from road signs than novices (Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), trained and professional male drivers demonstrate the highest levels of comprehension of symbolic road signs (Wontorczyk \u0026amp; Gaca, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings support the notion that drivers interpret road signs by drawing on similar past experiences (Stahl et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Stahl et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with their skill level influencing how quickly they are encoded in complex situations (Lappi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Advanced police drivers, for instance, not only process a broader range of relevant stimuli than age- and experience-matched controls and novices, but also do so more quickly (Crundall et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This is most likely due to their well-developed mental models of the driving environment, which are used to direct visual search (Crundall et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Underwood et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Underwood et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), manage speed (Walker et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and guide steering (Tuhkanen et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Environmental cues, including road signs, are thought to activate mental models that encompass causal and temporal relationships, also providing predictions about future developments of the situation (Durso et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; M\u0026uuml;hl et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The activation of these models relies on automatized processes, making them less susceptible to cognitive load (M\u0026uuml;hl et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, Baumann and colleagues (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found that under cognitive load, road signs preceding obstacles were less effective at reducing speed and increasing time to collision, when compared to undistracted driving conditions. Conversely, Charlton (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) showed that curve warnings emphasizing perceptual features of the curve (e.g., direction or severity) were effective in reducing the speed of cognitively loaded experienced drivers both when approaching and negotiating curves. While their design involved different combinations of road signs, including explicit speed instructions, such findings at least partially align with evidence that knowledge of environmental shape is key to successful navigation (McNamara, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although research suggests that road signs can aid implicit information processing (Charlton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and reduce uncertainty (M\u0026uuml;hl et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mushtaq et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), it remains unclear whether road signs that clearly reinforce spatial information by indicating the upcoming curve, its direction, and degree of turning can equally support the performance of both expert and non-expert drivers when negotiating geometrically challenging curves under conditions of high speed and cognitive load.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Study aim and hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this study was to examine if a road sign informing drivers of an upcoming curve would alter the performance of cognitively loaded expert and non-expert drivers while approaching and negotiating curves. Guided by theoretical models and existing literature, the study tested the following hypotheses:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cspan\u003e\n \u003cp\u003eBoth groups of drivers will resort to their optimal speed (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lehtonen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and increase steering corrections (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kountouriotis et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) under cognitive load, particularly when approaching curves.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e2. While both groups of drivers are expected to decrease their speed in response to the road sign (Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wontorczyk \u0026amp; Gaca, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this will be particularly evident among expert drivers when approaching curves, due to more accurate mental model of the curve ahead.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e3. While the road sign is not expected to mitigate the adverse effects of cognitive load on steering corrections and lane deviations, for either group, it will increase expert drivers\u0026rsquo; steering smoothness when approaching and negotiating curves, even under secondary task conditions, as the sign is expected to aid the prediction of the trajectory within a few seconds.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e4. Reflecting the automatic nature of road sign processing (e.g., Charlton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), the presence of such a cue is expected to support expert drivers\u0026apos; automatized driving skills, enabling them to maintain better secondary task performance.\u003c/p\u003e\n \u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eFollowing earlier research on driving expertise (Celic et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), two groups of male participants \u0026ndash; expert drivers (\u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;14; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e = 43.14, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.98) and non-expert drivers (\u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;20; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e = 38.05, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.77) \u0026ndash; took part in this study. Non-expert drivers were experienced non-professional drivers, recruited using the University of Leeds Driving Simulator (UoLDS) database. The expert driver group included UK advanced police drivers and firefighters, all of whom regularly drove during their work shift. Both groups were selected using identical criteria. However, the expert drivers had also completed an advanced driver training course (covering visual scanning, hazard perception, anticipation, and vehicle control, e.g., pursuit and emergency response driving) and held a UK advanced driving permit for at least 5 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.79, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.73). All participants drove more than 10000 miles per year (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eNon\u0026minus;experts\u003c/em\u003e\u003c/sub\u003e = 12794.61, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2075.12; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eExperts\u003c/em\u003e\u003c/sub\u003e = 17620.22, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6204.29) and had a valid driving license for more than 15 years (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eNon\u0026minus;experts\u003c/em\u003e\u003c/sub\u003e = 19.95, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.41; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eExperts\u003c/em\u003e\u003c/sub\u003e = 24.64, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.40). Informed consent was obtained from all participants who responded to the advert and they were compensated with \u0026pound;30 in cash. The project was approved by the School of Psychology Research Ethics Committee, University of Leeds (PSYC-622).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eApparatus\u003c/h3\u003e\n\u003cp\u003eThe study was conducted at the UoLDS, a Jaguar S-Type cabin encased by a spherical projection dome, with a 300\u0026deg; projection angle and 8 degrees of freedom motion system. Each experimental session included a 10-minute familiarization drive and a 20-minute main drive. All drives took place on a one-lane, 10-meter-wide road featuring identical curved segments, each preceded and followed by long straight sections. The characteristics of the curves were held constant throughout the experiment. Specifically, all curves were 432-meter-long hairpin curves with a consistent radius of 137 meters and no vertical curvature. The curve approach tangent was a 756-meter-long straight segment while the exit tangent was a 504-meter-long straight segment. To separate these curves, every exit tangent was followed by a 1500-meter-long filler section where no data was collected. The overall road design and curve characteristics were based on a previous study (Celic et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where these curves were identified as the most challenging for both non-expert and expert racing drivers. To minimize the risk of accidents and associated data loss, while still ensuring high-speed driving, vehicle speed was limited to 70 mph (112.65 kph), and drivers were instructed to reach this speed while approaching the curve. This value was chosen as it was close to the maximum speed at which the curve could be safely negotiated (75 mph). The simulated environment was set under consistent daytime lighting conditions across all trials and no other road objects, or oncoming traffic were present during the drive.\u003c/p\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eThe study followed a mixed model design and included one between-subject factor of Driving expertise (Experts, Non-experts) and two within-subject factors of Road sign (No sign, Sign), and Cognitive load (No task, 2-back task).\u003c/p\u003e \u003cp\u003eEvery participant drove through 8 hairpin curves, half of which were curved towards the left, and half to the right to control for the curve direction. Therefore, the experimental drive included four curve pairs presented under different combinations of the two within-subject factors, with every curve being separated by a filler segment. The assignment of experimental conditions (Baseline; 2-back task only; Road sign only; Road sign combined with 2-back task) was counterbalanced across participants to control for order and learning effects.\u003c/p\u003e \u003cp\u003eThe purpose of the road sign was to provide the preview of the upcoming curve, including information about its direction and the approximate degree of the required turn. It was presented for 1 second within the driving scene, 410 meters before the curve entry. The onset and duration of the projection were based on earlier research indicating that the stimulus needs to be presented early and long enough for drivers to perceive it, particularly when driving at high speeds (e.g., Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The onset of the projection was therefore specifically defined to ensure that the sign was perceived during the approach tangent, prior to both entering the curve and the onset of the secondary cognitive task. This design allowed the isolation of the effects of cognitive load on the processing and use of the sign information, rather than on its initial perception, and enabled the examination of changes in driving performance during both the approach and the negotiation of the low-visibility hairpin curve. The sign was shown in the standard order sign format, which is a white circle with a red border and a black symbol. Studies have found that, compared to other types of road signs, such signs prime the road scenes better due to their direct relationship with the behavioral response (Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The initial size of the sign was 96 x 96 pixels, and both its appearance and the information it conveyed remained constant throughout the experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), as the degree of curve turning did not vary.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeveral studies have suggested that cognitive load, mainly due to gaze concentration towards the road ahead, reduces drivers' ability to utilize explicit roadside instructions (Baumann et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Engstr\u0026ouml;m \u0026amp; Markkula, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Muttart et al., 2007). However, there is some evidence that head-up displays (HUDs) lower driver cognitive load and enhance driving performance (Zhu et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Teng et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, to approximate the presentation characteristics of a HUD and maximize sign visibility, the road sign in this study was projected directly into the drivers\u0026rsquo; forward field of view (central road area), just before the 2-back task started (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding cognitive load manipulation, the baseline drive did not include any secondary task, while the auditory-verbal version of the 2-back task (Mehler et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was used to induce high levels of cognitive load. Although more ecologically valid tasks, such as hands-free phone conversations, have frequently been used as concurrent cognitive activities in previous driving studies (Horberry et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; McEvoy et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Strayer \u0026amp; Johnston, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), the n-back task has emerged as a more controlled and easy-to-measure alternative (e.g., Fotios et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 2-back task started 400 meters before the curve entry and finished 400 meters after the curve exit. Participants heard a sequence of numbers (from 0 to 9, presented randomly) and repeated the number presented two numbers before the current one. The auditory stimuli were presented via in-vehicle speakers every 2.25 seconds, and a voice recorder was used to record responses. A schematic representation of the driving scenario is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e Upon arrival, all participants received verbal instructions about the experiment and, after signing the informed consent, they filled out a brief demographic questionnaire providing information about their age, years of driving, annual mileage, and, in the case of expert drivers, years holding an advanced driving permit. Participants then practiced the 2-back task and, once ready, they moved to the simulator. A 10-minute-long practice drive included segments of driving without any concurrent activity as well as segments where the 2-back task was presented. The experimenter was present during the practice drive to give instructions and answer questions, if required. After the experimenter left the simulator, the main drive started and lasted approximately 20 minutes per participant. Participants were instructed to reach 70 mph in a straight line and to continue driving as they normally would in a real-world setting. They were also instructed to perform the 2-back task as accurately and as quickly as possible. The complete session lasted \u0026sim;50 min per participant.\u003c/p\u003e\n\u003ch3\u003eMetrics\u003c/h3\u003e\n\u003cp\u003eLongitudinal performance was operationalized via mean and minimum speed. Minimum speed was a single value per participant and per road segment, averaged across participants to obtain the mean score.\u003c/p\u003e \u003cp\u003eRegarding lateral performance, steering wheel reversal rate (SWRR), steering smoothness, and standard deviation of lane position (SDLP), were calculated. SWRR, a measure of corrective steering, was calculated as the total number of steering reversals greater than 0.1 degrees, divided by the signal\u0026rsquo;s total length in minutes (Markkula \u0026amp; Engstr\u0026ouml;m, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). SDLP was used as an indicator of variations in drivers\u0026rsquo; lane position with respect to the centerline (Verster \u0026amp; Roth, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Steering smoothness was calculated as an absolute difference between the raw and smoothed steering wheel angle, with a smaller value indicating better performance (Lehtonen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Steering angle signal was smoothed using Sawitzky-Golay filter with 3 seconds moving window.\u003c/p\u003e \u003cp\u003eSecondary task performance was quantified as the percentage of missed and incorrect responses in the 2-back task. A response was considered missed if the participant failed to respond within a 2500 millisecond window following stimulus onset. A response was considered an error if the participant responded with an incorrect digit. The total number of misses and errors was summed and divided by the total number of stimuli presented, which served as the performance metric for the cognitive task.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSince the demand associated with driving the straight and curved segments of the road are different (Fitzpatrick et al., 2000; Serafin, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), separate data analyses were conducted for the 400-meter-long Approach tangent, and the 432-meter-long Curve sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA 2 (Driving expertise) \u0026times; 2 (Road sign) \u0026times; 2 (Cognitive load) Multivariate Analysis of Variance (MANOVA) was used to analyze lateral (SDLP, SWRR, steering smoothness) and longitudinal (mean and minimum speed) performance, given the multiple dependent variables. A 2 (Driving expertise) \u0026times; 2 (Road sign) Analysis of Variance (ANOVA) was used for the secondary task performance, as only one outcome measure was included (the percentage of misses).\u003c/p\u003e\n\u003cp\u003eSPSS v26 was used to conduct all the analyses. Type III (Marginal) Sum of Squares was chosen to calculate variances as this type accounts for unequal sample sizes by using harmonic rather than arithmetic mean. Bonferroni correction was applied to counteract the multiple comparisons problem and error bars indicate standard error. All descriptive statistics data tables (Appendix A) as well as those containing the complete set of results for longitudinal (Appendix B), lateral (Appendix C), and secondary task performance (Appendix C) are provided in the supplementary material.\u003c/p\u003e\n\u003cp\u003eRegarding MANOVA assumptions, the Kolmogorov-Smirnov test for the Approach tangent showed significant deflection from normality for 2 minimum speed distributions. Namely, when both the road sign and secondary activity were present, the minimum speed distributions of experts (\u003cem\u003ep\u003c/em\u003e = .01) and non-experts (\u003cem\u003ep\u003c/em\u003e = .004) were negatively skewed. Mean speed, on the other hand, did not show any deflections from normality. Levene\u0026rsquo;s tests showed that the error variance was unequal across groups in the case of minimum (\u003cem\u003ep\u003c/em\u003e = .04) and mean speed (\u003cem\u003ep\u003c/em\u003e = .05) in the baseline conditions. The assumption of homogeneity of the variance-covariance matrices was not violated. In terms of lateral performance, a deflection from normality was obtained for SDLP in the presence of the sign and secondary activity but only for non-expert drivers (\u003cem\u003ep\u003c/em\u003e = .009). The distribution was positively skewed. Levene\u0026rsquo;s tests showed that the error variance was equal across groups for all lateral performance indicators and the assumption of homogeneity of the variance-covariance matrices was not violated.\u003c/p\u003e\n\u003cp\u003eFor the Curved segment, longitudinal performance indicators did not show any deflections from normality and Levene\u0026rsquo;s tests showed that the error variance was equal across groups for both indicators. The assumption of homogeneity of the variance-covariance matrices was satisfied. On the other hand, SDLP and SWRR showed significant deflections from normality. SDLP in baseline condition in the case of non-experts (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and SWRR in the presence of the sign and secondary activity in the case of experts (\u003cem\u003ep\u003c/em\u003e = .01). All distributions were positively skewed. Levene\u0026rsquo;s tests showed equal error variance across groups for all lateral performance indicators and the assumption of homogeneity of the variance-covariance matrices was not violated.\u003c/p\u003e\n\u003cp\u003eGiven only slight deviations from the assumptions, MANOVA, known to be relatively robust to violations of normality, was considered appropriate for analyzing the data, and Wilks\u0026rsquo; lambda (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e) was utilized as an appropriate test statistic.\u003c/p\u003e\n\u003cp\u003eIn terms of secondary task performance, the Kolmogorov-Smirnov test did not show any significant deflection from normality for any of the two road segments. Levene\u0026rsquo;s tests showed that the error variance was equal across groups and the assumption of homogeneity of the variance-covariance matrices was satisfied. Given these results, ANOVA was deemed appropriate for the analysis.\u003c/p\u003e\n\u003ch3\u003eThe effects of cognitive load on expert and non-expert drivers\u0026apos; longitudinal and lateral performance\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eApproach tangent\u003c/h2\u003e\n \u003cp\u003eMixed-model MANOVA did not show any significant effects of Cognitive load on longitudinal performance of Expert and Non-expert drivers at the Approach tangent. The presence of the 2-back task did not affect drivers\u0026rsquo; minimum or mean speed, compared to No Task condition.\u003c/p\u003e\n \u003cp\u003eIn terms of lateral performance, mixed-model MANOVA showed a significant main effect of Cognitive load (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.452, \u003cem\u003eF\u003c/em\u003e(3,30)\u0026thinsp;=\u0026thinsp;12.11, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.55) for the Approach tangent.\u003c/p\u003e\n \u003cp\u003eUnivariate analysis showed that the main effect of Cognitive load was significant for SDLP (\u003cem\u003ep\u003c/em\u003e = .007, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.21), 0.1\u0026deg; SWRR (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.33), and steering smoothness (\u003cem\u003ep\u003c/em\u003e = .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.11). Lane deviations decreased (\u003cem\u003eM\u003c/em\u003e = .41, \u003cem\u003eSE\u003c/em\u003e = .02), small steering corrections increased (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61.70, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.82), and steering smoothness decreased (\u003cem\u003eM\u003c/em\u003e = .38, \u003cem\u003eSE\u003c/em\u003e = .02) in the 2-back condition, compared to No Task condition (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eSDLP\u003c/em\u003e\u003c/sub\u003e = .46, \u003cem\u003eSE\u003c/em\u003e = .03; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eSWRR\u003c/em\u003e\u003c/sub\u003e = 49.15, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.08; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eSmooth\u003c/em\u003e\u003c/sub\u003e = .32, \u003cem\u003eSE\u003c/em\u003e = .02), irrespective of Driving expertise.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eCurved segment\u003c/h2\u003e\n \u003cp\u003eMixed-model MANOVA revealed no significant effects of Cognitive load on longitudinal performance of Expert and Non-expert drivers during the Curved segment. The inclusion of the 2-back task did not influence drivers\u0026rsquo; minimum or mean speed compared to the No Task condition.\u003c/p\u003e\n \u003cp\u003eWhen a mixed-model MANOVA was run for the lateral performance indicators, the main effect of Cognitive load (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.441, \u003cem\u003eF\u003c/em\u003e(3,30)\u0026thinsp;=\u0026thinsp;12.65, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.56) was found significant. Further analyses showed that the main effect of Cognitive load was significant for 0.1-degree SWRR (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.47), and almost reached significance in the case of steering smoothness (\u003cem\u003ep\u003c/em\u003e = .06). As Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows, in Curved segments, the number of small steering corrections increased in the 2-back (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;68.24, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.29) compared to No Task conditions (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58.84, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.18), irrespective of Driving expertise.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eThe effects of road sign on expert and non-expert drivers\u0026apos; longitudinal and lateral performance\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003eApproach tangent\u003c/h2\u003e\n \u003cp\u003eWhen a mixed-model MANOVA was run for the longitudinal performance at the Approach tangent, the main effects of Driving expertise (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.810, \u003cem\u003eF\u003c/em\u003e(2,31)\u0026thinsp;=\u0026thinsp;3.31, \u003cem\u003ep\u003c/em\u003e = .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.17), and Road sign (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.711, \u003cem\u003eF\u003c/em\u003e(2,31)\u0026thinsp;=\u0026thinsp;6.31, \u003cem\u003ep\u003c/em\u003e = .005, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.29) were found to be statistically significant. Moreover, a significant interaction effect was seen between Road sign and Expertise (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.792, \u003cem\u003eF\u003c/em\u003e(2,31)\u0026thinsp;=\u0026thinsp;4.06, \u003cem\u003ep\u003c/em\u003e = .03, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.21), suggesting that the effects of Road sign on longitudinal performance were different for Expert and Non-expert drivers.\u003c/p\u003e\n \u003cp\u003eUnivariate analyses showed that, compared to the No Sign condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;63.63, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85), presence of the Sign (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61.24, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.04) significantly decreased drivers\u0026rsquo; minimum speed (\u003cem\u003ep\u003c/em\u003e = .002, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.27). Additionally, while approaching the curve, Expert drivers\u0026rsquo; minimum speed (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;63.64, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.35) was significantly higher (\u003cem\u003ep\u003c/em\u003e = .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.16) when compared to that seen for Non-experts (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61.23, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.13).\u003c/p\u003e\n \u003cp\u003eOn the other hand, the Road sign by Expertise interaction effect was significant only for mean speed (\u003cem\u003ep\u003c/em\u003e \u0026lt; .01, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.18). As shown in Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, mean approach speed was lower in the Sign, compared to No Sign condition, but only for Expert drivers. While the mean speed of Experts was higher than the mean speed of Non-experts when there was No Sign, differences in mean speed between these two groups were not found in the Sign condition.\u003c/p\u003e\n \u003cp\u003eIn terms of lateral performance, no main or interaction effects of Road sign and Driving expertise were found statistically significant for the Approach tangent.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eCurved segment\u003c/h2\u003e\n \u003cp\u003eMixed-model MANOVA performed on the two longitudinal performance indicators showed a significant main effect of Road sign (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.698, \u003cem\u003eF\u003c/em\u003e(2,31)\u0026thinsp;=\u0026thinsp;6.70, \u003cem\u003ep\u003c/em\u003e = .004, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.30) and a significant main effect of Expertise (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.770, \u003cem\u003eF\u003c/em\u003e(2,31)\u0026thinsp;=\u0026thinsp;3.72, \u003cem\u003ep\u003c/em\u003e = .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.14). No other main or interaction effects were obtained.\u003c/p\u003e\n \u003cp\u003eUnivariate analyses found that the main effect of Road sign was significant for the minimum (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.30) and mean speed (\u003cem\u003ep\u003c/em\u003e = .006, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.22). In both cases, the speed in Curved segments decreased in the Sign (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emean\u003c/em\u003e\u003c/sub\u003e = 55.78, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.11; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e = 51.60, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.22), compared to No Sign condition (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emean\u003c/em\u003e\u003c/sub\u003e = 57.19, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e = 53.34, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.07), irrespective of Driving expertise.\u003c/p\u003e\n \u003cp\u003eSimilarly, the main effect of Driving expertise was found significant for the minimum (\u003cem\u003ep\u003c/em\u003e = .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.11) and mean speed (\u003cem\u003ep\u003c/em\u003e = .04, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.12). Expert drivers negotiated curves with higher minimum (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54.69, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.73) and mean speed (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58.56, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.54) compared to Non-experts (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e = 50.34, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.45; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emean\u003c/em\u003e\u003c/sub\u003e = 54.33, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.29), irrespective of Road sign. Since the same pattern of changes was obtained for both speed indicators, Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the main effects of Expertise and Road sign on minimum speed only.\u003c/p\u003e\n \u003cp\u003eIn the case of lateral performance in Curved segments, MANOVA yielded a significant main effect of Road sign (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.712, \u003cem\u003eF\u003c/em\u003e(3,30)\u0026thinsp;=\u0026thinsp;2.71, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.19).\u003c/p\u003e\n \u003cp\u003eFollowing further analysis, the main effect of Road sign was significant for SDLP (\u003cem\u003ep\u003c/em\u003e = .01, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.18) and steering smoothness (\u003cem\u003ep\u003c/em\u003e = .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.10). Displayed in Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, when the Sign was present, lane deviations in Curves decreased (\u003cem\u003eM\u003c/em\u003e = .431, \u003cem\u003eSE\u003c/em\u003e = .02) and steering smoothness increased (\u003cem\u003eM\u003c/em\u003e = .736, \u003cem\u003eSE\u003c/em\u003e = .07) compared to No Sign drives (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eSDLP\u003c/em\u003e\u003c/sub\u003e = 496, \u003cem\u003eSE\u003c/em\u003e = .03; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eSmooth\u003c/em\u003e\u003c/sub\u003e = .890, \u003cem\u003eSE\u003c/em\u003e = .08).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe effects of road sign on expert and non-expert drivers\u0026apos; longitudinal and lateral performance with and without the 2-back task\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eApproach tangent\u003c/h2\u003e\n \u003cp\u003eIn terms of longitudinal performance, a mixed-model MANOVA did not show any significant interaction effects between Cognitive load and Road sign at the Approach tangent.\u003c/p\u003e\n \u003cp\u003eOn the other hand, a significant Cognitive load x Road sign interaction effect (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.781, \u003cem\u003eF\u003c/em\u003e(3,30)\u0026thinsp;=\u0026thinsp;2.94, \u003cem\u003ep\u003c/em\u003e = .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.21) on lateral performance was obtained for the Approach tangent.\u003c/p\u003e\n \u003cp\u003eThe Cognitive load by Road sign interaction effect was found to be significant only for SDLP (\u003cem\u003ep\u003c/em\u003e = .05, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.10). As displayed in Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, compared to the No Task condition, the 2-back decreased SDLP only when the Sign was not present. In the absence of the 2-back task, the Sign decreased SDLP, compared to No Sign condition. However, differences between the two Road sign conditions were not obtained when the 2-back task was introduced.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eCurved segment\u003c/h2\u003e\n \u003cp\u003eFor both longitudinal and lateral performance indicators, mixed-model MANOVAs did not reveal any significant interaction effects between Cognitive load and Road sign at the Curved segment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eThe effects of driving expertise and road sign on secondary task performance\u003c/h2\u003e\n \u003cp\u003eA 2 (Experts, Non-experts) x 2 (No Sign, Sign) mixed-model ANOVA on the percentage of misses was run separately for the two road segments. For both the Approach tangent and Curved segment, the Kolmogorov-Smirnov test did not show any significant deflection from normality. Levene\u0026rsquo;s tests showed that the error variance was equal across groups and the assumption of homogeneity of the variance-covariance matrices was satisfied. Given these results, ANOVA was deemed appropriate for the analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eApproach tangent\u003c/h2\u003e\n \u003cp\u003eNo statistically significant main or interaction effects were found when the 2x2 ANOVA was performed on the percentage of misses. This indicates that Experts performed the same as Non-experts in the 2-back task, and the presence of the Sign did not affect their performance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eCurved segment\u003c/h2\u003e\n \u003cp\u003eA 2x2 ANOVA for the percentage of misses while negotiating curves showed a significant Road sign x Expertise interaction effect (Wilks\u0026rsquo; \u003cem\u003e\u0026Lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.762, \u003cem\u003eF\u003c/em\u003e(1,32)\u0026thinsp;=\u0026thinsp;3.52, \u003cem\u003ep\u003c/em\u003e = .04, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.13). As shown in Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the percentage of misses did not differ between Experts and Non-experts in the No Sign condition. Only Expert drivers\u0026rsquo; percentage of misses decreased when the Sign was shown. Due to this, Expert drivers had a lower percentage of misses compared to Non-experts in the presence of the Sign.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis driving simulator study explored if the use of a road sign that provides a preview of an upcoming curve alters longitudinal and lateral performance of cognitively loaded expert and non-expert drivers, while approaching and negotiating curves.\u003c/p\u003e \u003cp\u003eBased on previous findings (Liao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muttart et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), expert drivers were expected to decrease their speed more than non-experts, when engaged in a secondary task. Although expert drivers maintained a higher minimum speed at the approach tangent, and higher mean speed at both the approach and curved segments, we found no additional changes in speed during the secondary task conditions. Research has shown that, with experience, drivers automatically maintain an optimal speed, tuned to the road and driving conditions (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lewis-Evans et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Recarte \u0026amp; Nunes, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Deviations from this optimal speed require cognitive resources, and if these resources are being used by another, secondary activity, drivers tend to resort to their optimal speed (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, it seems that both groups already drove at their optimal speed, allowing them to perform the 2-back task equally well without the need for any speed adjustments. Conversely, in the presence of the road sign, both groups reduced their minimum speed in the curve approach, and both minimum and mean speed were reduced while negotiating the curve. This supports several earlier studies which have shown that road signs can prime drivers ahead of curves with a limited sight distance (Charlton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Charlton (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), for instance, showed that road signs similar to the one used in this study successfully reduced the speed of cognitively loaded drivers, when they approached and negotiated tight curves at 45 kph (\u0026asymp;\u0026thinsp;28 mph). However, this was not always seen for wider curves, driven at 65 kph (\u0026asymp;\u0026thinsp;40 mph) and 85 kph (\u0026asymp;\u0026thinsp;53 mph). Although speed is thought to diminish the positive effects of road signs (Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), the present study demonstrates that road signs can still prompt a speed reduction even at higher speeds (70 mph), provided they are presented with sufficient lead time. In this case, the distance available before entering the curve was sufficient for drivers to process the information and initiate a deceleration response. Moreover, the road sign used in this study reduced the driving speed of both groups, irrespective of cognitive load.\u003c/p\u003e \u003cp\u003eThe road sign was expected to decrease expert drivers\u0026rsquo; speed more than non-experts\u0026rsquo;, in both road segments. However, this difference was only seen for the approach tangent. This anticipatory behavior was expected by highly skilled drivers, due to their more accurate mental representation of the upcoming curve, triggered by the road sign (Crundall et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Stahl et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further support for this explanation comes from the secondary task performance results, indicating that, when prompted by a sign ad driving through the curve, expert drivers\u0026rsquo; misses decreased, and they also missed less responses to the n-back compared to non-experts. On the other hand, previous research has shown that less skilled drivers often adopt a reactive rather than proactive driving style (Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which could explain late reaction to the road sign of non-expert drivers, who reduced their speed only in curves. However, the modest differences in minimum and mean speed between expert and non-expert drivers during the approach and curved segments (ranging from 2 to 5 mph), along with a difference in missed n-back responses in curves of around 12%, suggest that distinctions between these two groups are relatively subtle, particularly when compared to the more pronounced differences typically reported between novices and experienced drivers (e.g., Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of lateral performance, the commonly reported increase in small steering corrections and decrease in deviations from the centerline was observed when drivers were engaged in a cognitive secondary activity (Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kountouriotis et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but only for the approach segment. Based on the active gaze hypothesis, which suggests that drivers tend to steer in the direction of their gaze (Robertshaw \u0026amp; Wilkie, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wilkie et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wilkie et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), increased micro-steering adjustments and a consequent decrease in deviations from the centerline are linked to the increased concentration of gaze towards the road center, for cognitively loaded drivers (Kountouriotis \u0026amp; Merat, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kountouriotis et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As such a relationship between steering corrections and lane deviations was not found for the curved segments, with the 2-back condition only increasing the number of steering corrections, our initial hypothesis was only partially confirmed. Previous studies have shown that when negotiating tight curves, drivers typically maintain a more central position than required, as this allows them to have a good peripheral view of both road edges (Land \u0026amp; Horwood, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Robertshaw \u0026amp; Wilkie, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this study, both groups of drivers already drove quite close to the centerline, which may explain the absence of changes in lane deviations in the curved segments due to cognitive load.\u003c/p\u003e \u003cp\u003eCognitive load decreased steering smoothness of both expert and non-expert drivers at the approach tangent. This might imply that the gaze concentration effect influences the collection of far-road information used by drivers (and especially experts, see: Crundall et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Land \u0026amp; Tatler, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lappi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; van Leeuwen et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to preview upcoming changes in direction in order to maintain smooth steering (Boer, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Donges, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Okafuji et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Salvucci \u0026amp; Gray, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). As with lane deviations, a decrease in steering smoothness was not obtained for the curved segment, suggesting that further experimentation is needed to understand how lateral position is maintained in curves, particularly when drivers are cognitively loaded.\u003c/p\u003e \u003cp\u003eIn support of the initial hypothesis, the road sign did not affect steering corrections for any road segment but increased steering smoothness for the curved segment. Whereas steering smoothness relies on far-road information, steering corrections are believed to be based on information obtained from the near-road region (Salvucci \u0026amp; Gray, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The purpose of the sign was to facilitate the prediction of the trajectory in the timeframe of a few seconds, thus facilitating the perception of far-road information and increasing steering smoothness, as seen in this study. Moreover, when drivers were negotiating curves, the sign increased their steering smoothness and decreased lane deviations, irrespective of cognitive load level. Supporting earlier findings (Charlton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), this can be the advantage of such road signs, assisting drivers even when their resources are taxed by another cognitive activity. From an applied perspective, curve preview cues appear to facilitate path planning by activating representations of upcoming road geometry, even under cognitive load. This supports \u0026ldquo;just-in-time\u0026rdquo; information delivery approaches, where early activation of mental models guides attention. Integrating these principles into HUDs and driver assistance systems may aid cognitive offloading in demanding situations (e.g., low-visibility curves), with potential safety benefits.\u003c/p\u003e \u003cp\u003eAlthough more skilled drivers, who are generally faster at identifying road signs and whose responses are often automatized (Babić et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Borowsky et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Koyuncu \u0026amp; Amado, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), are expected to benefit more than less skilled drivers when road scenes are primed by road signs, this study did not show many differences in performance between expert and non-expert (but experienced) drivers, irrespective of cognitive load. It should be noted, however, that a larger sample size would likely have increased the statistical power of the analyses, improving the ability to detect subtle but potentially meaningful differences between groups. Moreover, the findings suggest that the relationship between skill automatization and driving experience or expertise may not be strictly linear. It is possible that there is a threshold in the development of automaticity beyond which further driving experience or training does not substantially enhance automatic performance. This could at least partially explain the limited differences observed in speed and steering control between expert and non-expert, but experienced drivers. Importantly, these findings also challenge prevailing theoretical perspectives on driving expertise, which posit that driving expertise, typically acquired through deliberate and structured training, is qualitatively distinct from mere driving experience (Ericsson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). In contrast, the present results suggest that driving expertise may not be entirely domain-general, but rather task-specific. That is, the advantages of highly trained drivers (such as advanced police drivers included in this sample) may be most evident in specific driving tasks, such as hazard perception, anticipatory scanning, or high-speed pursuit and emergency response driving, rather than in more general measures of vehicle control under standard conditions.\u003c/p\u003e \u003cp\u003eFuture studies could benefit from more precisely matching the experience levels (e.g., years of driving or mileage) between expert and non-expert groups, but also from including less experienced drivers to assess the effectiveness of road signs. Additionally, future research could leverage gaze patterns, which are well-established indicators of increased cognitive load (Lehtonen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and are particularly informative when considering driving experience (Lehtonen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Underwood et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) or expertise (Pammer \u0026amp; Blink, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pammer et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Namely, it is still unclear how cognitive load affects experts\u0026rsquo; gaze patterns and whether such changes can be directly associated with their driving performance.\u003c/p\u003e \u003cp\u003eThe inclusion of only one type of road sign and only one type of curve is a limitation of this study, as there is evidence that the perception and effectiveness of signs depend on the road context (Vilchez, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vilchez, 2019). Road signs are thought to enhance driving performance in environments lacking sufficient cues, such as curves with limited sight distance (Crundall \u0026amp; Underwood, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). It is, therefore, possible that the road sign used in this study provided information that was redundant to cues already available in the environment, which may have diminished observable differences in performance. Future studies could benefit from incorporating less predictable curves to explore whether such road signs act as cues which effectively elicit automatized behaviors, particularly under cognitive load. Moreover, systematic variations of both road geometry, width, environmental visual cues, and sign characteristics (e.g., symbolic versus textual signs, early versus late placement) might provide valuable insights into how different sign-curve combinations influence driver behavior under cognitive load. Such work would help clarify the boundary conditions under which road signs function most effectively and support the practical application of these findings across varied real-world driving contexts.\u003c/p\u003e \u003cp\u003eWhile the n-back task remains a widely used and controllable method for inducing cognitive load in experimental settings (Mehler et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), its ecological validity may be limited when compared to more naturalistic cognitive distractions, such as hands-free phone conversations or interactions with passengers. These real-world situations often involve social, emotional, or unpredictable elements that are not fully captured by the structured and repetitive nature of the n-back task. Future research might, therefore, incorporate more realistic secondary tasks to assess whether the effects observed here generalize to such real-world driving scenarios.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile researchers have theorized that cognitive load disproportionately affects the performance of drivers of different skill levels (e.g., Engstr\u0026ouml;m et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fuller, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), there is limited empirical evidence to support such a notion. This study explored the combined effects of cognitive load and a road sign showing the preview of an upcoming curve on the performance of expert and non-expert drivers. Expert drivers showed superior longitudinal performance compared to non-experts in the absence of cognitive load. However, neither group\u0026rsquo;s speed was reduced by engagement in the cognitively loading task. Regarding lateral performance, both groups showed compensatory behaviors when driving and performing the 2-back task, including increased small steering adjustments, reduced steering smoothness, and decreased deviations from the centerline, mostly obtained at the curve approach tangent. The absence of significant lateral performance differences in the curved segment, despite changes in steering corrections and smoothness at the approach tangent, underscores the need for further investigation regarding how drivers maintain lateral position under cognitive load.\u003c/p\u003e \u003cp\u003eThe road sign proved effective in prompting speed reductions and enhancing steering smoothness, suggesting its utility even in cognitively demanding situations. Moreover, the road sign led to improved 2-back task performance of expert drivers, when negotiating curves. However, the nuanced differences between expert and non-expert drivers, particularly in speed adjustments and secondary task performance when exposed to the road sign underline the need to consider driving expertise as task-specific rather than purely domain-general. These findings highlight the importance of expanding the sample to include a greater number of expert drivers, while also incorporating less experienced drivers, examining performance across more targeted tasks, such as hazard perception and anticipatory scanning, that may be more sensitive to differences in expertise.\u003c/p\u003e \u003cp\u003eWhile the study demonstrated the effectiveness of road signs in enhancing driving performance, the inclusion of only one road sign and one curve type limits its generalizability. Future research should explore a broader range of curve geometries and incorporate more unpredictable road environments to better understand the role of road signs as effective cues for automatic vehicle responses. The use of more ecologically valid secondary tasks would provide deeper insights into how skill level and cognitive load interact to influence driving performance. Gaze data can further illuminate the relationship between cognitive load, driving expertise, and driving performance, offering practical implications for road design and driver training programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding and Competing interests \u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article. No funding was received to assist with the preparation of this manuscript and the authors declare they have no financial interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMC: Conceptualization, Methodology, Investigation, Formal analysis, Visualization, Writing \u0026ndash; Original draft preparation. JB: Supervision, Project administration, Writing \u0026ndash; Reviewing and Editing. NM: Supervision, Project administration, Writing \u0026ndash; Reviewing and Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was conducted as part of a collaborative PhD project between Rimac Technology LLC and the Institute for Transport Studies at the University of Leeds. The authors would like to thank Michael Daly, Anthony Horrobin, and Albert Solernou Crusat for creating experimental scenarios and supporting data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset used in this study is not publicly available, as it is owned by Rimac Technology LLC. However, it may be made available upon reasonable request and with permission from Rimac Technology LLC.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBabić D, Tremski Š, Babić D (2019) Investigation of traffic signs understanding \u0026ndash; Eye tracking case study. Tehnički Vjesn 26(1):29\u0026ndash;35\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaumann MRK, Petzoldt T, Groenwoud C, Hogema J, Krems J (2008) The effect of cognitive tasks on predicting events in traffic. 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Springer International Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-78227-6_20\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-78227-6_20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eAppendix A.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eDescriptive statistics for longitudinal performance indicators for the Approach tangent and Curved segment\u003c/span\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eDriving expertise\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCognitive load\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMinimum speed (mph)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMean speed (mph)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eN\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eM\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSD\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRange\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eM\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSD\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRange\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eApproach tangent\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpert drivers\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2-back task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e61.90\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.62\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e48.79\u0026ndash;68.92\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e67.58\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.39\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e60.14\u0026ndash;70.01\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e65.47\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4.36\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e55.17\u0026ndash;69.38\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e68.59\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.60\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e60.48\u0026ndash;69.96\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e62.53\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5.71\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e52.85\u0026ndash;67.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e67.46\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.47\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e57.54\u0026ndash;69.65\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e64.63\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4.28\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e56.29\u0026ndash;68.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e68.37\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.47\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e62.83\u0026ndash;69.89\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNon-expert drivers\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2-back task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e61.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e9.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e31.45\u0026ndash;68.58\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e68.55\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.70\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e59.79\u0026ndash;69.91\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e62.06\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6.92\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e32.67\u0026ndash;69.14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e67.64\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.21\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e58.42\u0026ndash;69.31\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e59.43\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.42\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e40.53\u0026ndash;69.31\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e67.65\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.90\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e56.29\u0026ndash;69.94\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e62.34\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.12\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e36.34\u0026ndash;69.33\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e67.67\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e58.41\u0026ndash;69.58\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCurved segment\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpert drivers\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2-back task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e53.38\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6.77\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e43.74\u0026ndash;65.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e57.50\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5.21\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e47.31\u0026ndash;67.39\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e56.40\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6.26\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e47.44\u0026ndash;68.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e59.95\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4.81\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e53.68\u0026ndash;69.54\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e54.13\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5.34\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e44.69\u0026ndash;62.52\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e58.11\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4.30\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e50.21\u0026ndash;65.25\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e54.81\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5.45\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e48.32\u0026ndash;62.74\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e58.65\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3.82\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e52.59\u0026ndash;65.33\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNon-expert drivers\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2-back task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e50.11\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e8.44\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e36.58\u0026ndash;69.10\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e54.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.90\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e38.17\u0026ndash;68.15\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e51.90\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.33\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e35.67\u0026ndash;65.87\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e55.55\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.46\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e43.60\u0026ndash;68.79\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e48.89\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e8.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e38.31\u0026ndash;66.32\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e53.24\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e44.78\u0026ndash;68.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e50.46\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e33.15\u0026ndash;66.63\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e54.66\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.78\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e38.75\u0026ndash;68.15\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eDescriptive statistics for lateral performance indicators for the Approach tangent and Curved segment\u003c/span\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eDriving expertise\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCognitive load\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSDLP (m)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.1\u0026deg; SWRR (deg)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSteering smoothness (deg)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eN\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eM\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSD\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRange\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eM\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSD\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRange\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eM\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSD\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRange\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eApproach tangent\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpert drivers\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2-back task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.45\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.23\u0026ndash;0.76\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e65.44\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e25.69\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e29.48\u0026ndash;118.92\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.42\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.23\u0026ndash;0.74\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.45\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.23\u0026ndash;0.66\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e65.96\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e23.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e29.54\u0026ndash;122.67\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.37\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.14\u0026ndash;0.81\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.45\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.24\u0026ndash;0.61\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e53.44\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20.45\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e25.46\u0026ndash;78.75\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.34\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.13\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.14\u0026ndash;0.60\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.57\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.22\u0026ndash;0.88\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e54.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e21.25\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e21.52\u0026ndash;91.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.35\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.18\u0026ndash;0.63\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNon-expert drivers\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2-back task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.36\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.15\u0026ndash;0.89\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e54.51\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e24.32\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e18.32\u0026ndash;110.89\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.31\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.15\u0026ndash;0.54\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.36\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.18\u0026ndash;0.85\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e60.88\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20.23\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e39.64\u0026ndash;101.88\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.32\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.18\u0026ndash;0.59\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.41\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.13\u0026ndash;0.79\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e44.89\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e17.62\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e10.78\u0026ndash;76.60\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.27\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.1\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.13\u0026ndash;0.54\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.42\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.08\u0026ndash;1.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e44.19\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e17.11\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e18.44\u0026ndash;81.92\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.31\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.11\u0026ndash;1.00\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCurved segment\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpert drivers\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2-back task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.46\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.19\u0026ndash;0.90\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e71.1\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20.75\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e50.91\u0026ndash;122.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.9\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.27\u0026ndash;2.38\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.51\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.29\u0026ndash;0.81\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e72.95\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e18.85\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e44.38\u0026ndash;108.45\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.23\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.36\u0026ndash;2.49\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.46\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.20\u0026ndash;0.79\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e60.41\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e19.56\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e30.76\u0026ndash;95.59\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.62\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.20\u0026ndash;1.78\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.55\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.26\u0026ndash;0.86\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e58.48\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e18.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e43.00\u0026ndash;103.34\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.84\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.21\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.22\u0026ndash;2.44\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNon-expert drivers\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2-back task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.41\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.13\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.23\u0026ndash;0.71\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e67.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e23.72\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e39.42\u0026ndash;140.74\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.82\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.23\u0026ndash;2.89\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.42\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.16\u0026ndash;0.97\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e61.89\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e15.63\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e41.20\u0026ndash;107.86\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.84\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.30\u0026ndash;2.15\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo task\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.39\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.13\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.24\u0026ndash;0.73\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e57.38\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e20.28\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e28.21\u0026ndash;111.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.61\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.24\u0026ndash;2.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNo sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.5\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.26\u0026ndash;1.13\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e59.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e18.61\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e35.28\u0026ndash;96.53\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.86\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.26\u0026ndash;1.70\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003cstrong\u003eAppendix B.\u003c/strong\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eThe results of univariate tests for longitudinal performance indicators for the Approach tangent and Curved segment\u003c/span\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eApproach tangent\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCurved segment\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMin speed\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eF (df)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026eta;2\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026pi;\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eF (df)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026eta;2\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026pi;\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e2.86 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.05\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.16\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.69\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e3.71 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.05\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.11\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.46\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign (RS)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e11.54 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.002\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.27\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.91\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e13.8 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;.001\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.30\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.95\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCognitive load (CL)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.19 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.67\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.006\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.44 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.24\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.04\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.21\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.40 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.53\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.01\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.10\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.86\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.001\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.10 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.75\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.003\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.39 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.54\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.01\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.09\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.04 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.84\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.001\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.08 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.31\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.17\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.25 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.31\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.75 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.40\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.13\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMean speed\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.83\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.001\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e4.31 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.04\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.12\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.52\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.97 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.33\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e8.85 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.006\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.22\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.82\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.11 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.30\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.18\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.72 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.40\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.13\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e7.12 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.01\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.18\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.74\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.001 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.97\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.00\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.20 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.66\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.006\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.13 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.72\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.004\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.46 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.50\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.01\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.10\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.95 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.34\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.16\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.75 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.39\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.13\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.81 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.38\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.14\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eNote\u003c/span\u003e. \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026eta;2\u003c/span\u003e\u0026thinsp;=\u0026thinsp;partial eta square, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026pi;\u003c/span\u003e\u0026thinsp;=\u0026thinsp;statistical power.\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eAppendix C.\u003c/strong\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eThe results of univariate tests for lateral and secondary task performance indicators for the Approach tangent and Curved segment\u003c/span\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eApproach tangent\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCurved segment\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSDLP\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eF (df)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026eta;2\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026pi;\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eF (df)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026eta;2\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026pi;\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3.69 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.10\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.44\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.7 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.11\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.36\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign (RS)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.33 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.32\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e6.72 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.01\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.17\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.71\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCognitive load (CL)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e8.33 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.007\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.21\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.78\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.96 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.33\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.16\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.37 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.25\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.04\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.21\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.82\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.002\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.16 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.69\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.005\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.87\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.001\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e3.97 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.05\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.16\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.49\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.12 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.29\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.18\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.73 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.25\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.19 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.66\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.006\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.07\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.1\u0026deg; SWRR\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.94 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.27\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.50 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.49\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.11\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.94 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.34\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.31 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.58\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.01\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.08\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e15.61 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;.001\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.33\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.97\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e27.86 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;.001\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.47\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.99\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.41 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.53\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.01\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.28 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.60\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.009\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.08\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.04 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.85\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.001\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3.19 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.41\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.70 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.41\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.13\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.26 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.62\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.008\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.08\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.74 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.39\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.13\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3.12 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.40\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSteering smoothness\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3.08 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.40\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.66 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.006\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.07\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.87\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.001\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e3.84 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.05\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.14\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.45\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e3.88 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.05\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.15\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.45\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3.56 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colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.51\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.04 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.83\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.001\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.57 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.45\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.11\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.56 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.46\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.11\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign x CL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.93 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.27\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.12 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.30\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.18\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x CL x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.25 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.62\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.008\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.14 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.71\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.004\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.06\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ePercentage of misses\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.37 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.55\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.01\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.09\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.50 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.48\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.11\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRoad sign\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.19 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.66\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.006\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.67 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.45\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.11\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExpertise x RS\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.007 (1,32)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.93\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.00\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e3.52 (1,32)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.04\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.13\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e.35\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eNote. \u0026eta;2\u003c/span\u003e\u0026thinsp;=\u0026thinsp;partial eta square, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003e\u0026pi;\u003c/span\u003e\u0026thinsp;=\u0026thinsp;statistical power.\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cognition-technology-and-work","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctwo","sideBox":"Learn more about [Cognition, Technology \u0026 Work](http://link.springer.com/journal/10111)","snPcode":"10111","submissionUrl":"https://submission.nature.com/new-submission/10111/3","title":"Cognition, Technology \u0026 Work","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"curve negotiation, curve preview, directional cue, cognitive load, driving expertise","lastPublishedDoi":"10.21203/rs.3.rs-9449373/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9449373/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe lack of theoretical and empirical coherence makes it difficult to ascertain how cognitive load, induced by a cognitive secondary activity, impacts the driving performance of non-expert and expert drivers. Even less is known about strategies that could mitigate potential impairments, precluding the development of effective countermeasures. Fourteen UK advanced police drivers (experts), and twenty experienced non-professional drivers (non-experts), were recruited for this study to examine whether a road sign previewing an upcoming curve could reduce the effects of cognitive load on driving performance. Differences between experts and non-experts were observed only in longitudinal control, while cognitive load primarily affected lateral control. When the road sign was present, experts reduced their speed both when approaching and negotiating curves, whereas non-experts slowed only within the curve. Although the road sign had a minimal impact on lane deviations when approaching curves under cognitive load, it effectively reduced deviations and improved steering smoothness in curves, irrespective of cognitive load. These findings underscore the task-specific nature of driving expertise and suggest that anticipatory visual cues can enhance safety and performance of expert and non-expert drivers even in situations when they are cognitively loaded.\u003c/p\u003e","manuscriptTitle":"Can a preview of an upcoming curve mitigate the effects of cognitive load on expert and non-expert drivers’ vehicle control?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 10:26:54","doi":"10.21203/rs.3.rs-9449373/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"164434643043345359145120612305522779518","date":"2026-05-18T19:55:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-29T13:16:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T12:55:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T12:55:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cognition, Technology \u0026 Work","date":"2026-04-17T12:46:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cognition-technology-and-work","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctwo","sideBox":"Learn more about [Cognition, Technology \u0026 Work](http://link.springer.com/journal/10111)","snPcode":"10111","submissionUrl":"https://submission.nature.com/new-submission/10111/3","title":"Cognition, Technology \u0026 Work","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0d043558-fad4-4f09-a196-a9e05a38584f","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"164434643043345359145120612305522779518","date":"2026-05-18T19:55:13+00:00","index":20,"fulltext":""},{"type":"reviewersInvited","content":"13","date":"2026-04-29T13:16:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T12:55:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T12:55:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T10:26:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 10:26:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9449373","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9449373","identity":"rs-9449373","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}