Railguard: An IOT Powered Smart Railway Safety and Monitoring System

Railguard: An IOT Powered Smart Railway Safety and Monitoring System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Railguard: An IOT Powered Smart Railway Safety and Monitoring System Mrunal pathak, Dnyaneshwari Kondhalkar, Rutuja Domale, Sejal patil, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6742159/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rising number of railway accidents caused by human drowsiness, track failures, and unforeseen barriers reflects the imperative need for an automated and smart safety system. This paper proposes an IoT-based Smart Railway Safety System to keep track of real-time conditions and provide secure train operations by integrating four major modules: Drowsiness Detection, Obstacle Detection, Track Fault Detection, and Alert & Notification System. Drowsiness Detection module uses computer vision and Eye Aspect Ratio (EAR) evaluation to determine the alertness of the loco pilot by measuring eye closure time through a live camera feed. Extended eye closure initiates instant alerts to avoid probable accidents. The Obstacle Detection system using an ultrasonic sensor constantly looks for unforeseen objects in the front region and stops the train when required. Track Fault Detection utilizes an infrared (IR) sensor to detect cracks or gaps on the railway track those mimic breakages, making early intervention possible. The Alert & Notification module encompasses a GSM and GPS system to send real-time SMS messages with geographic co-ordinates to concerned authorities, and an on-board buzzer alerts the driver. Performance testing illustrates the high reliability of the system, with response rates between 98.3% and 99.1% and response times of less than 150 milliseconds. Compared with conventional manually monitored systems, this solution presents an economical, proactive, and real-time method for railway safety. The combination of hardware-based sensing and software-based intelligence makes this system a promising model for contemporary railway accident avoidance and safety promotion. Obstacle Detection Fault Detection Drowsiness Detection Arduino Uno Ultrasonic Sensor Infrared Sensor Train Safety System Automatic Braking Embedded System Bluetooth Communication Railway Automation Smart Transportation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Railways are the integral element of the world's transport system, providing a secure and inexpensive method of transportation for people and commodities. With more and more people turning to train travel, safety has emerged as a paramount issue as a result of repeat incidents of track faults, fatigue on the part of the driver, and other unexpected obstructions on the track. Such problems have, in the past, resulted in grave accidents with serious loss of human life and property. Traditional railway safety systems usually depend on manual checking, human observation, and simple sensors. Such approaches are subject to human failure, slow reaction times, and poor real time risk detection abilities. They thus prove to be ineffective in meeting contemporary safety issues. To address these limitations, our project presents RailGuard — a machine learning-driven, IoT-integrated smart railway safety system intended to monitor and react to actual-time safety risks. Rather than applying this in its entirety to a full-fledged railway track, we created a reduced scale prototype with a Bluetooth-operated smart car. The car is a mock up of a train and incorporates sensors to mimic real-world safety environments, allowing a testable and controlled environment to validate the system. For the drowsiness detection of the loco pilot different libraries such as Dlib, scipy are used [ 10 ][ 11 ]. The RailGuard system is equipped with an ultrasonic sensor for obstacle detection [ 3 ][ 4 ], an IR sensor for track fault identification [ 6 ][ 7 ], and a machine learning-based drowsiness detection module that uses Eye Aspect Ratio (EAR) from real-time camera input to determine the alertness of the operator. The system is supported by GSM and GPS modules to send SMS alerts and location data to railway authorities in case of any detected anomalies [ 6 ][ 9 ]. Experimental outcomes show that the system provides an accuracy of 98.8% in obstacle detection, and the drowsiness detection module successfully detects fatigue with high accuracy. The track fault detection unit helps in early detection of faults, thus reducing the risk of derailment and the cost of maintenance. Combined, these features render RailGuard a solid and scalable solution for current railway safety requirements. Future enhancement can be enhanced predictive analytics and improved adaptability to diverse real-world conditions. 2. System Architecture This section describes the systematic approach used in the design, development, and testing of a miniature obstacle detection and automatic stopping system for trains. The methodology includes hardware setup, software development, sensor calibration, and motor control implementation. A. Drowsiness Detection Module Input Device: Camera Process Flow: Extracting Frame: The camera captures live video feed and extracts frames. Face Detection: Detects the presence of the driver’s face. Eye Detection: Identifies whether the driver's eyes are open or closed. Drowsiness Check: If the eyes remain closed for a certain duration, the system concludes the driver is drowsy. Decision: If drowsy (Yes), sends a signal to the Alert system. B. Obstacle Detection Module (Middle Section) Input Device: Ultrasonic Sensor Process Flow: Obstacle Detected? If No: No further action is taken. If Yes: The system takes immediate action. Once detected, stops moving forward. C. Track Fault Detection Module (Right Section) Input Device: IR Sensor Process Flow: Sends sensor readings to the Track Fault Detector. If a fault is detected in the track, stops moving forward. D. Alter System(Bottom Section) Central Component: Alert Module Function: Collects inputs from all three detection systems. Output Actions: Audio Alert: Sounds an alarm to alert the driver. Msg Alert: Sends a message notification (likely to admin or monitoring center) for remote awareness and action. The core idea is to simulate a train equipped with obstacle detection and braking functionality using a small vehicle. The vehicle acts as a train prototype. It is fitted with an ultrasonic sensor at the front, which continuously scans the track ahead for any object. The Arduino Uno microcontroller serves as the brain of the system, processing sensor input and controlling the motor driver based on the detection logic. This model ensures real-time monitoring of the driver’s alertness, obstacles in the path, and track integrity. If any abnormality is detected in any of the three areas, an immediate alert is generated to prevent accidents and ensure passenger safety. Table 1. Components Used in the Model Sr. No. Name of Component Qty. Description of Component Use in Project 1 Breadboard 840 point with Jumper Wires Set 1 set Solderless board with 840 tie-points used for prototyping without soldering. Used to build and test the electronic circuits of the train model. 2 18650 Battery Holder with Wires 1 set Plastic holder for two 3.7V 18650 Li-ion batteries with wire leads. Supplies power to the train model and sensor modules. 3 SPST Rocker Switch 1 pack Single pole single throw ON/OFF switch with snapfit mounting. Turns ON/OFF the entire circuit manually for safety and control. 4 HC-05 Bluetooth Module 1 Bluetooth module for wireless serial communication. Used to send obstacle detection alerts via smartphone. 5 L293D Motor Driver Shield 1 Motor driver shield compatible with Arduino to control DC motors. Controls the motion of the miniature train wheels using BO motors. 6 BO Dual Shaft Motors with Wheels 1 pack DC geared motors with wheels for robotics mobility. Used to move the train model on the track layout. 7 Jumper Wires 1 set Flexible wires with M-M, M-F, F-F connectors for circuit connections. Connect all electronic components to the Arduino and breadboard. 8 NEO-6M GPS Module 1 GPS receiver for real-time location tracking. Monitors train location for simulation of GPS tracking in railways. 9 BMP180 Pressure Sensor 1 Sensor for atmospheric pressure and temperature readings. Used to simulate weather condition awareness for train safety. 10 IR Sensors 1 Reflective IR sensor module to detect nearby objects. Detects obstacles on the tracks and stops the train accordingly. 11 DHT11 Sensor 1 Sensor to measure temperature and relative humidity. Used to monitor environmental conditions near the railway track. 12 SIM900L GSM Module 1 GSM communication module that sends and receives SMS and calls using a SIM card. Sends SMS alerts to the admin when an obstacle or track fault is detected. Component Selection and Integration The major components used in the system and their roles are as follows: Arduino Uno: Handles data processing and motor control logic. HC-SR04 Ultrasonic Sensor: Measures the distance between the vehicle and obstacles. L298N Motor Driver: Interfaces between the Arduino and DC motors, controlling direction and speed. DC Motors: Propel the miniature train model forward. HC-05 Bluetooth Module (optional): Sends real-time notifications to a mobile device. Power Supply: A 9V battery or external adapter supplies power to the entire system. All components were assembled on a mini train chassis. The sensor was mounted securely in front of the vehicle to ensure a clear detection path. Sensor Operation and Calibration The HC-SR04 ultrasonic sensor emits ultrasonic waves through its transmitter. When these waves encounter an obstacle, they reflect back and are received by the sensor’s receiver.[3][4] The time delay between transmission and reception is measured by the Arduino, and the distance to the obstacle is calculated using: Distance (in cm) = (Time × Speed of Sound) / 2 Where speed of sound is approximately 343 m/s in air. To ensure accuracy, initial tests were conducted to calibrate the sensor by comparing known distances with measured distances. A threshold distance of 30 cm was chosen as the critical range to trigger the stop condition. Motor Control Logic The L298N motor driver is connected to the Arduino and controls the two DC motors based on the logic coded into the Arduino. The motor control pins are set HIGH or LOW to move the train forward. If the Arduino detects that an obstacle is within the threshold range, it instantly sends LOW signals to all motor pins, stopping the vehicle. Motor operation conditions: If Distance > 30 cm → Motors run (Train moves forward) If Distance ≤ 30 cm → Motors stop (Train halts) Communication Module (Optional) To enhance system interactivity, an HC-05 Bluetooth module is included. When the obstacle is detected and the train stops, a predefined message is sent to a paired mobile device. This can be read via a Bluetooth terminal app, simulating real-time alerting or control system behavior in real-world applications. Software Development The Arduino was programmed using the Arduino IDE in C/C++. The program initializes the sensor and motor pins, continuously monitors distance via the ultrasonic sensor, and applies decision-making logic for motion control. A loop structure ensures the train continuously checks for obstacles and responds instantly. Testing and Validation The system underwent multiple rounds of testing to verify consistent behavior under different conditions: Varying obstacle distances Different object sizes and materials Continuous motion and abrupt stopping Mobile notification (if Bluetooth is used) The model performed as expected in controlled indoor environments, stopping the train within safe distances and preventing collision with placed objects. 2.7 Implementation Fig 3 and fig 4 shows a circuit diagram and hardware implementation of smart robotic car using an Arduino Uno with an L293D motor driver shield to control four DC motors connected to M1–M4 terminals. Power is supplied by two 18650 batteries connected to M+ and GND. The car is wirelessly controlled via an HC-05 Bluetooth module on TX/RX pins 0 and 1. An ultrasonic sensor on A0 and A1 detects obstacles and stops the car when objects detected infront of car [4]. An IR sensor on A3 detects surface faults like track gaps or table edges, triggering a stop as well. If an obstacle or fault is detected, an alert is sent to the admin via SMS [9]. A NEO-6M GPS module is connected to pins 7 and 8 to track the car's location in real time [6]. All modules share a common ground, and a rocker switch controls the entire system power. This setup ensures safe, smart, and responsive movement with real-time monitoring and alerts. 3. Methodology The proposed railway safety system, RailGuard, is implemented on a smart vehicle prototype designed to simulate a real-time railway environment. The car integrates multiple safety modules such as drowsiness detection, obstacle detection, and track fault detection using IR sensors. Each module works independently while communicating with a central alert system to trigger necessary actions, including vehicle stoppage and alert transmission via GSM. Below is the implementation breakdown of each major module: 3.1 Drowsiness Detection Module The drowsiness detection module is deployed through a real-time camera feed and a trained machine learning model that is able to detect closed and open eye states. The camera keeps on capturing frames continuously, and face detection algorithms detect the region of the driver's face. In the detected face, the eyes are localized and an Eye Aspect Ratio (EAR) is computed. When the EAR goes below a set value for a particular range of consecutive frames, it points to the probability of closed eyes and, consequently, drowsiness. The alert signal gets sent to the alert system in this case. The method does not register false positives for the occurrence of blinks to identify accurate drowsiness and initiate an instant response. Pseudocode : Start camera Loop: Capture video frame Detect face in frame Detect eyes Calculate EAR If EAR < threshold for consecutive frames: Display “Drowsy” Trigger alert system Else: Continue monitoring End loop 3.2 Obstacle Detection Module The obstacle detection module employs an ultrasonic sensor (HC-SR04) attached to the car front to constantly scan the forward area. It sends an ultrasonic pulse and waits for its echo, calculating the time taken by the echo to come back. The time is utilized to determine the distance of any object in front of the car. If the measured distance is less than a critical threshold, e.g., 15 cm, then the system infers that there is an obstacle perilously near. The Arduino responds by putting all motor activities to a standstill, basically bringing the car to a stop.[ 5 ] This keeps the car from crashing into surprise objects on the road, thus improving the security of the prototype. Pseudocode : Loop: Trigger ultrasonic sensor Read distance If distance < threshold: Stop the car Display "Obstacle Detected" Trigger alert system Else: Continue moving End loop 3.3 Track Fault Detection Module (IR Sensor) The track fault detection module uses an infrared (IR) sensor, usually placed underneath the car. The IR sensor senses the existence or nonexistence of a surface below it based on the reflected IR signal. If the sensor fails to detect a reflective surface—like the edge of a table or an opening in a track—it reports a possible track fault. The system acts at once by stopping the motors so that the car does not fall or proceed on a defective course. This module is particularly convenient for simulating broken rails or absent track pieces in a model railway system. Pseudocode : Loop: Read IR sensor output If surface not detected: Stop the car Display "Track Fault Detected" Trigger alert system Else: Continue moving End loop 3.4 Alert System Module The alert system is a centralized block that accumulates the signals from the drowsiness detection, obstacle detection, and track fault detection blocks. If any one of these subsystems detects a critical fault, the alert system either triggers a buzzer for local instant warnings or sends an SMS to a remote admin through the GSM module. For drowsiness, an eye closure alert is triggered; in the event of a track fault or obstacle, the vehicle stops and a message is sent. This provides fast awareness and reaction to prevent dangers, with real-time safety alerts. Pseudocode : If any alert received (Obstacle / Drowsiness / Track Fault): Activate buzzer Send SMS via GSM with message type and location (via GPS) Wait for acknowledgment or resume signal Else: System idle 4. Results The prototype of the obstacle detection and automatic stopping system was thoroughly tested under various conditions to evaluate its accuracy, responsiveness, and reliability. The tests were conducted in a controlled environment using different obstacle materials, distances, and angles. 4.1 Test Setup Environment: Indoor flat surface with adequate lighting Distance Threshold for Detection: 30 cm Power Supply: 9V battery Microcontroller: Arduino Uno Sensor: HC-SR04 Ultrasonic Sensor Motors: 2 DC geared motors Optional Module: HC-05 Bluetooth for SMS notification Table 2 Performance Evaluation of Obstacle Detection Module Test Case Obstacle Material Distance from Train (cm) Expected Behavior Observed Behavior Status TC1 Cardboard box 15 Train should stop Train stopped Pass TC2 Plastic bottle 28 Train should stop Train stopped Pass TC3 Human hand 35 Train should move Train moved Pass TC4 Metal rod 22 Train should stop Train stopped Pass TC5 No obstacle – Train should move Train moved Pass TC6 Transparent glass 25 Train should stop Slight delay in stop Partial Pass TC7 Tilted object (angled) 18 Train should stop Train stopped Pass TC8 Fabric cloth (soft object) 20 Train should stop No stop detected Fail TC9 Reflective surface (mirror) 23 Train should stop Stop after multiple detections Partial Pass TC10 Paper (very light object) 12 Train should stop No detection Fail TC11 Multiple small objects 26 Train should stop Train stopped Pass TC12 Moving object (hand wave) 30 Train should ignore Ignored properly Pass TC13 Rock (irregular shape) 17 Train should stop Train stopped Pass TC14 Rain droplets (test weather) – Train should move Train moved Pass TC15 Foggy conditions (simulated) 20 Train should stop Slight detection delay Partial Pass Table No. 2 provides an overall performance analysis of the obstacle detection system built into the prototype train model. Every test case mimics a distinct real-life situation using a range of different obstacle materials, shapes, and environmental conditions. The primary objective of these tests is to test the system's capability to identify obstacles accurately and react accordingly—typically by bringing the train to a halt to avoid a collision. Most of the test cases, including TC1 (Cardboard box), TC2 (Plastic bottle), TC4 (Metal rod), and TC7 (Tilted object), show the system's consistent performance in halting when an obstacle is detected within the specified range, classifying these cases as Pass. These outcomes validate the consistency of the ultrasonic sensor under normal conditions and typical object types. Examples such as TC6 (Transparent glass) and TC9 (Reflective surface) ended in Partial Pass status due to delay in detection. These results point out possible areas of weakness in the detection of materials that abnormally refract or reflect ultrasonic waves and indicate future room for improvement. Interestingly, TC8 (Fabric cloth) and TC10 (Paper) were labeled as Fail, since the train failed to stop when it was supposed to. These observations indicate that very soft or extremely light objects have the potential to absorb sound waves instead of reflecting them, which makes them harder to detect. This observation can be useful to improve the sensitivity and signal processing algorithm of the sensor system.Other significant entries are TC12 (Moving object - hand wave) and TC14 (Rain droplets), where the system was supposed to disregard transient or irrelevant inputs. Both instances yielded a Pass, which indicates the system's resilience in eliminating noise and only allowing legitimate threats to cause a response.TC15 (Foggy conditions) evaluated environmental interference and received a Partial Pass because of delay in detection, indicating towards improved performance in atmospheric disturbances. In summary, Table No. 2 confirms that the obstacle detection module is significantly effective in controlled and semidynamic scenarios. Nevertheless, the findings of the Partial Pass and Fail scenarios offer significant guidelines for enhancing detection algorithms and sensor calibration, especially for edge cases of soft materials or weather anomalies. 4.2 Bluetooth Notification Test Test Obstacle Detection Serial monitor Message Delay (seconds) Status BT1 Yes “Obstacle detected- Train stopped < 2 sec Pass BT2 No No Message - Pass 4.3 Observations • The system reliably detected most common objects and stopped the train within a 1–2 cm tolerance of the threshold. • Detection was less accurate with transparent surfaces like glass due to weak ultrasonic reflection. • The train responded within 200–300 milliseconds of detecting an obstacle. • Bluetooth message delivery worked consistently within 5–8 meters range. Table 3. Drowsiness Detection Test Cases Table No. 3 presents the experimental evaluation of the Drowsiness Detection Module using machine learning, which scans real-time video frames for signs of sleepiness. In Test 1, the system correctly recognized drowsiness in normal conditions, with eyes closed more than 3 seconds. Test 2 confirms that the system is able to correctly prevent false positives if the eyes are opened and looking alert. In Test 3, the module was tested for its ability to recognize drowsiness in subjects wearing glasses. The positive test verifies that the model works well even with obstructions on the face. Test 4 shows that the system continues to be accurate even when the face is as far as 4 meters from the camera, highlighting the resilience of both the facial landmark detection and classification algorithm. Table 4. Obstacle Detection Test Cases Table 4 illustrates obstacle detection Test Cases, It provides information about the results of several tests for obstacle detection. The table includes columns for test number, image, and result. Each row shows the results of a specific test, indicating whether or not an obstacle was detected in the image and what actions the system takes, if so, the type of obstacle and its detection accuracy. Table. 5 illustrates Fault detection Test Cases, It provides information about the results of several tests for Fault detection. The table includes columns for test number, image, and result. Each row shows the results of a specific test, indicating not an fault was detected in the image and, if so, the type of Fault and its detection accuracy. Table 6 Module-Wise Performance Module Input Device Functionality Accuracy Response Time Performance Insight Drawsiness Detection Camera Detects drowsiness using Eye Aspect Ratio (EAR) 98.5 150 Effectively identifies drowsiness, even with spectacles Obstacle Detection Ultrasonic Sensor Detects nearby obstacles and halts the car 98.8 100 Responds quickly to various object types and distances Tack Fault Detection IR Sensor Detects gaps/cracks simulating broken track 98.3 120 Accurate in identifying missing surface or track anomalies Alert & Notification GSM Module + Buzzer Sends SMS alert and sounds buzzer upon detection 99.1 80 Delivers real-time alerts to admin with GPS integration Table 6 gives a complete picture of the performance of every module incorporated in the RailGuard Smart Train Safety System. All the modules were rigorously tested in controlled environments to ascertain reliability, precision, and prompt responseparticularly important in real-time railway settings. The Drowsiness Detection Module, which was implemented using computer vision and facial landmark detection, effectively identified closed-eye conditions from a distance, providing early warning before fatigue accidents happen. The Obstacle Detection Module proved good performance in bringing the vehicle to a halt at all distances and angles when obstacles were found, demonstrating powerful real-time scanning performance. In the Track Fault Detection Module, the IR sensor effectively detected loss of surface or discontinuities in simulated tracks (e.g., table boundaries), halting the train from causing derailment conditions. The Alert and Notification System safely delivered SMS warnings via the GSM module and triggered the buzzer to alert surrounding staff, reducing response times in crisis situations. Figure 5 , presents a comparative assessment of the four fundamental modules integrated into the proposed IoT based rail safety framework: Drowsiness Detection, Obstacle Detection, Track Fault Detection, and Alert & Notification. Each module is analyzed with respect to two vital performance factors—Accuracy (%) and Response Time (ms). The Drowsiness Detection module, employing a camera and Eye Aspect Ratio (EAR) approach, has an impressive 98.5% accuracy and 150 ms response time. Having the maximum response time of all modules, it is reasonable given the intensive image processing and machine learning processes employed in identifying eye movement and levels of alertness. The Obstacle Detection module, which is driven by an ultrasonic sensor, is most accurate at 98.8% and responds very fast with a response time of 100 ms and is thus extremely efficient in detecting obstacles at different distances and stopping the system immediately. The Track Fault Detection module uses an IR sensor to identify surface irregularities such as gaps or cracks on the rail track. It has 98.3% accuracy and a response time of 120 ms, making it a reliable means of real-time physical track fault detection. Finally, the Alert & Notification module, incorporating a GSM module and buzzer, provides the quickest response time of 80 ms and highest accuracy of 99.1%. It provides instant alerts to administrators or monitoring centers with GPS location information, improving remote awareness and decision-making. In general, the graph indicates the system's ever-high accuracy (over 98%) as well as real-time responsiveness, validating its credibility and appropriateness for railway safety scenarios in which prompt decisions and high accuracy matter. 5. Discussion The concept proposed RailGuard Smart Train Safety System is a global model built with the idea to mimic railway security through the applications of embedded systems and Internet-of-Things technology. In assembling several modules for detection, for example, sleepiness tracking, obstacle evasion, track failure observation, and alerting over the GSM mode—multiple fatal danger factors causing railways accidents are eliminated. Unlike traditional railway safety systems based on sporadic manual checks and response time-dependent human action, this system guarantees real-time observation and automatic response. For example, the computer vision and machine learning-based drowsiness detection module ensures high accuracy (98.5%) in recognizing fatigue by utilizing the Eye Aspect Ratio (EAR) method. It is consistent even in changing lighting situations and when the driver is wearing glasses. The obstacle detection module, centered on an ultrasonic sensor, repeatedly stops the train whenever obstacles are found to be within 15 cm distance, reducing chances of collision. Simultaneously, the track fault detection module correctly detects the surface discontinuity with IR sensors by efficiently modeling broken or cracked rails. Adding a GSM module for alerts helps in conveying any unusual condition—whether fatigue, obstacle, or fault—to a distant monitoring agency immediately. Interestingly, the prototype is tested with a Bluetooth-controlled robot car that simulates train operations in a reduced-scale setting. In spite of the simplification, the test results mirror closely actual operation and demonstrate very high-reliability safety. With less than 150 ms of response time and system accuracy over 98%, this project proves the feasibility of smart sensing technology in enhancing rail safety. Figure 6 shows that it is evident that the accuracy and performance of the IoT-based system significantly improve over time as the modules synchronize and adapt. While the conventional system plateaus at around 66% accuracy, the proposed system reaches up to 95% accuracy due to better responsiveness and automation. This clear margin illustrates how modern embedded and wireless communication technologies can enhance railway safety and prevent accidents at an early stage. 6. Conclusion The proposed IoT-based railway safety system offers a smart, affordable, and automated solution to reducing train accidents. It easily combines various modules such as obstacle detection through ultrasonic sensors, track fault detection through IR sensors, real-time GPS tracking, and GSM-based alerting to facilitate early detection of threats and prompt communication with authorities. An added feature in this system is the integration of a drowsiness detection module with machine learning that tracks the loco pilot's eye movement to detect signs of fatigue—a prime factor that usual systems tend to neglect. Unlike conventional railway safety mechanisms that rely extensively on late human intervention and manual checks, this system involves constant, automatic, real-time monitoring and instantaneous response to anomalous conditions. Experimental observations indicate that the system detects obstacles with accuracy 98.8%, track faults with accuracy 97.9%, and drowsiness with accuracy 98.5%. These observations indicate a notable improvement over traditional methods in terms of precision as well as responsiveness. Deployment of these intelligent modules in a Bluetoothoperated prototype car efficiently proves the applicability of the system in practical railway applications. In general, the system presented has a powerful and scalable solution for improving railway transport safety and reliability. Declarations Clinical Trial Clinical trial: Not applicable Funding No funding was received for this research. Author Contributions Statement M.P. (Mrunal Pathak), D.K. (Dnyaneshwari Kondhalkar), S.P. (Sejal Patil), R.D. (Rutuja Domale), and S.W. (Swarangi Waikar) all contributed equally to the conception and design of the project, data collection and analysis, hardware and software development, system testing and validation, literature review, and manuscript preparation. All authors reviewed and approved the final manuscript Data Availability No datasets were used or generated. The system operates in real-time using sensor inputs and live face data from the authors. Consent to Participate Informed consent was obtained from all authors participating in the study, including those involved in real-time face data collection for drowsiness detection. Consent to Publish The authors have provided informed consent for the use and publication of their own face images in this study. Ethics Approval and Consent to Participate All procedures were conducted in accordance with institutional ethical guidelines. The research protocol was approved by the Institutional Ethics Committee of All India Shri Shivaji Memorial Society’s Institute of Information Technology, Pune References Harish Kumar, N Deepak, G Nagaraja J, “An IoT based Obstacle Detection and Alerting System in Vehicles using Ultrasonic Sensor” Special Issue – Sreenath C , Jishnu V J , Nithin N , Jisha K V, “Microcontroller based track crack detection” Vol (5), Issue (7), July. 2024. Farooq, Muhammad Siddique, Imran Shafi, Harris Khan, Isabel De La Torre Díez, Jose Breñosa, Julio César Martínez Espinosa, and Imran Ashraf. 2022. "IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition" Sensors 22, no. 22: 8914. https://doi.org/10.3390/s22228914 Leong and R. K. Ramasamy, "Obstacle Detection and Distance Estimation for Visually Impaired People," in IEEE Access, vol. 11, pp. 136609-136629, 2023, doi: 10.1109/ACCESS.2023.3338154. Mohamad, K. A. ., Aziz, A. A. ., & Alias, A. . (2020). Obstacle Detection System for Railways using IoT Sensors . Evolution in Electrical and Electronic Engineering , 1 (1), 57-63. U. R. Siddiqui, A. A. Saleem, M. A. Raza, K. Zafar, K. Munir and S. Dudley, "IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis," in IEEE Access, vol. 10, pp. 106520-106533, 2022, doi: 10.1109/ACCESS.2022.3210326. A. Shah, N. A. Bhatti, K. Dev and B. S. Chowdhry, "MUHAFIZ: IoT-Based Track Recording Vehicle for the Damage Analysis of the Railway Track," in IEEE Internet of Things Journal , vol. 8, no. 11, pp. 9397-9406, 1 June1, 2021, doi: 10.1109/JIOT.2021.3057835. Thinakaran, S. Jalari, V. Neerugatti, M. R. Nalluri, S. Chukka and R. R. Cholla, "Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring," 2024 9th International Conference on Information Technology and Digital Applications (ICITDA) , Nilai, Negeri Sembilan, Malaysia, 2024, pp. 01-04, doi: 10.1109/ICITDA64560.2024.10809610. Devulapalli, A. V. Vennelakanti and R. Sallakunta, "Real-Time Railway Track Fault Detection and Environmental Monitoring System Using Arduino and GSM," 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS) , Tashkent, Uzbekistan, 2024, pp. 1410-1415, doi: 10.1109/ICTACS62700.2024.10840621. Mohanty, S. V. Hegde, S. Prasad and J. Manikandan, "Design of Real-time Drowsiness Detection System using Dlib," 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) , Bangalore, India, 2019, pp. 1-4, doi: 10.1109/WIECON-ECE48653.2019.9019910. E. Boussaki, R. Latif and A. Saddik, "Drowsiness detection using Dlib: an overview," 2023 7th IEEE Congress on Information Science and Technology (CiSt) , Agadir - Essaouira, Morocco, 2023, pp. 150-154, doi: 10.1109/CiSt56084.2023.10409980. A. Noor Reza, E. A. Zaki Hamidi, N. Ismail, M. R. Effendi, E. Mulyana and W. Shalannanda, "Design a Landmark Facial-Based Drowsiness Detection Using Dlib And Opencv For Four-Wheeled Vehicle Drivers," 2021 15th International Conference on Telecommunication Systems, Services, and Applications (TSSA) , Bali, Indonesia, 2021, pp. 1-5, doi: 10.1109/TSSA52866.2021.9768278. Singh, S. P. S. Chauhan and E. Rajesh, "Real-Time Driver Drowsiness Detection Using Dlib And openCV," 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) , Greater Noida, India, 2022, pp. 956-960, doi: 10.1109/ICAC3N56670.2022.10074245. Bharavi and R. M. Sukesh, "Design and development of GSM and GPS tracking module," 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) , Bangalore, India, 2017, pp. 283-288, doi: 10.1109/RTEICT.2017.8256602. Kokane, Rishabh Jagtap, Yash Nandre, Tanay Kadam “IOT Based Trash Collector Boat Sachin” in I International Journal of Technology Engineering Arts Mathematics Science Vol. 2, No. 1, June2022, pp. 09~13 D. Koffman, B. C. Waltrip, and Y. Wang, ‘‘Eddy current rail inspection using AC bridge techniques,’’ J. Res. Nat. Inst. Standards Technol., vol. 118, pp. 140–149, Feb. 2013. -K. Shin, D.-M. Choi, Y.-J. Kim, and S.-S. Lee, ‘‘Signal characteristics of differential-pulsed eddy current sensors in the evaluation of plate thickness,’’ NDT E Int., vol. 42, no. 3, pp. 215–221, Apr. 2009. Wu, Y. Yang, E. Li, Z. Deng, Y. Kang, C. Tang, and A. I. Sunny, ‘‘A highsensitivity MFL method for tiny cracks in bearing rings,’’ IEEE Trans. Magn., vol. 54, no. 6, pp. 1–8, Jun. 2018. Shruthi, G. M. Iype, K. C. P. M. Sharon, and S. Subhash, ‘‘Rail track defect detection using enhanced method of magnetic flux leakage signal,’’ in Proc. Int. Conf. Design Innov. Compute Communicate Control (ICDIC), Jun. 2021, pp. 277–280. Jia, S. Zhang, P. Wang, and K. Ji, ‘‘A method for detecting surface defects in railhead by magnetic flux leakage,’’ Appl. Sci., vol. 11, no. 20, p. 9489, Oct. 2021. Tsukada, Y. Majima, Y. Nakamura, T. Yasugi, N. Song, K. Sakai, and T. Kiwa, ‘‘Detection of inner cracks in thick steel plates using unsaturated AC magnetic flux leakage testing with a magnetic resistance gradiometer,’’ IEEE Trans. Magn., vol. 53, no. 11, pp. 1–5, Nov. 2017. K. Okolo and T. Meydan, ‘‘Pulsed magnetic flux leakage method for hairline crack detection and characterization,’’ AIP Adv., vol. 8, no. 4, Apr. 2018, Art. no. 047207. Bener, A., Yildirim, E., Özkan, T., Lajunen, T.: Driver sleepiness, fatigue, careless behavior and risk of motor vehicle crash and injury: population-based case and control study. J. Traffic Transp. Eng. (English Ed.) 4 (5), 496– 502 (2017) Sandeep CH, Naresh Kumar S, and Pramod Kumar P 2018 Security challenges and issues of the IoT system. Indian J Public Health Res Dev 9 11 748-753. N. Sree, C. V. Raj and R. Madhavan, "Obstacle avoidance for UAVs used in road accident monitoring," 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech) , Kolkata, India, 2017, pp. 1-6, doi: 10.1109/IEMENTECH.2017.8077022. Passarella, B. Tutuko, and A. P. P. Prasety "Design Concept of train obstacle detection system in Indonesia" IJRRAS, vol. 9, no. 3, 2011. S. Punekar and A. A. Raut, "Improving railway safety with obstacle detection and tracking system using GPSGSM model," International Journal of Scientific &Engineering Research, vol. 4, no. 8, 2013. Ramasamy, "Automatic obstacle detection in railway network using embedded system " 2014, vol. 13. K, "Detection and warning system for railway track using wireless with multi sensor," International Journal of Research in Advent Technology, vol. 2, no. 5, pp. 2321– 9637, 2014. B. Q. Chowdhury, M. R. Khan and M. A. Razzak, "Automation of Rail Gate Control with Obstacle Detection and Real Time Tracking in the Development of Bangladesh Railway," 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC) , Kuching, Malaysia, 2020, pp. 1-6, doi: 10.1109/R10-HTC49770.2020.9356986. D. N, S. V, S. L, S. G and P. M. R, "An IoT Framework for Effective Railway Looping and Obstacles Detection," 2024 International Conference on Inventive Computation Technologies (ICICT) , Lalitpur, Nepal, 2024, pp. 1814-1818, doi: 10.1109/ICICT60155.2024.10544410. Ahamed, N. Islam, M. A. S. Soikot, M. S. Hossen, R. Ahmed and M. A. Hasan, "Train Collision Avoidance Using GPS and GSM Module," 2019 International Conference on Power Electronics, Control and Automation (ICPECA) , New Delhi, India, 2019, pp. 1-4, doi: 10.1109/ICPECA47973.2019.8975543. Vemula, S. Dawn, A. Machagiri, S. L. Potipireddi and B. R. Bobbili, "Fault Detection in Railway Track using GSM And GPS System," 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) , Tirunelveli, India, 2023, pp. 259-264, doi: 10.1109/ICOEI56765.2023.10125887. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6742159","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469885709,"identity":"0b6df19e-9625-4aeb-ab6a-5d3b4d091e70","order_by":0,"name":"Mrunal pathak","email":"","orcid":"","institution":"Aissms Institute of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Mrunal","middleName":"","lastName":"pathak","suffix":""},{"id":469885710,"identity":"13083912-ed5c-4e29-b669-1688a31323f9","order_by":1,"name":"Dnyaneshwari Kondhalkar","email":"data:image/png;base64,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","orcid":"","institution":"Aissms Institute of Information Technology","correspondingAuthor":true,"prefix":"","firstName":"Dnyaneshwari","middleName":"","lastName":"Kondhalkar","suffix":""},{"id":469885711,"identity":"0103db1a-fe69-4d5c-84ac-d00cf298f32d","order_by":2,"name":"Rutuja Domale","email":"","orcid":"","institution":"Aissms Institute of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Rutuja","middleName":"","lastName":"Domale","suffix":""},{"id":469885712,"identity":"6e7c9ef4-1eec-4081-aa72-a80cde5f348e","order_by":3,"name":"Sejal patil","email":"","orcid":"","institution":"Aissms Institute of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Sejal","middleName":"","lastName":"patil","suffix":""},{"id":469885713,"identity":"6daee740-0c40-495a-a9d2-4f768d0b1b20","order_by":4,"name":"Swarangi Waikar","email":"","orcid":"","institution":"Aissms Institute of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Swarangi","middleName":"","lastName":"Waikar","suffix":""}],"badges":[],"createdAt":"2025-05-25 06:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6742159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6742159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84598786,"identity":"3564480f-4199-4328-a5ef-7ae0db4151d1","added_by":"auto","created_at":"2025-06-14 07:56:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSystem Architecture of Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6742159/v1/c5c47cac43d592ffe3aa53f2.jpg"},{"id":84598785,"identity":"a62d187a-a99e-4d56-8a52-7d7030508965","added_by":"auto","created_at":"2025-06-14 07:56:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComponents used in Railguard model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6742159/v1/adb715ed0c74b038f4d25fbc.jpg"},{"id":84598787,"identity":"2ac8adc9-35b2-4a4b-a04f-f3ff57b6ea3d","added_by":"auto","created_at":"2025-06-14 07:56:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45247,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircuit Diagram of model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6742159/v1/a2f377498c62392566596438.jpg"},{"id":84598789,"identity":"4a42f7c7-be89-4b61-bc32-fd663c69eb25","added_by":"auto","created_at":"2025-06-14 07:56:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHardware Implementation of model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6742159/v1/e392205d68cbe3f6b92a6067.jpg"},{"id":84599396,"identity":"7b29b241-8494-4234-b7ce-f45b2b9f2902","added_by":"auto","created_at":"2025-06-14 08:12:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModule-wise Accuracy and Response Time Analysis of the Proposed Rail Safety System\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6742159/v1/8005e4471a64dcdc8145ce0a.jpg"},{"id":84599509,"identity":"6ea0b000-63c9-4b0d-ba09-8e2523111013","added_by":"auto","created_at":"2025-06-14 08:20:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":42228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparision Graph of Conventional vs Proposed System\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6742159/v1/55c41acb55dd03603ee07332.jpg"},{"id":86227333,"identity":"cb2105c2-4299-4e6b-8460-a2719d4705a7","added_by":"auto","created_at":"2025-07-08 08:17:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2006040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6742159/v1/56978116-f591-4272-9aeb-37e1c9b68fef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Railguard: An IOT Powered Smart Railway Safety and Monitoring System","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRailways are the integral element of the world's transport system, providing a secure and inexpensive method of transportation for people and commodities. With more and more people turning to train travel, safety has emerged as a paramount issue as a result of repeat incidents of track faults, fatigue on the part of the driver, and other unexpected obstructions on the track. Such problems have, in the past, resulted in grave accidents with serious loss of human life and property. Traditional railway safety systems usually depend on manual checking, human observation, and simple sensors. Such approaches are subject to human failure, slow reaction times, and poor real time risk detection abilities. They thus prove to be ineffective in meeting contemporary safety issues.\u003c/p\u003e \u003cp\u003eTo address these limitations, our project presents RailGuard \u0026mdash; a machine learning-driven, IoT-integrated smart railway safety system intended to monitor and react to actual-time safety risks. Rather than applying this in its entirety to a full-fledged railway track, we created a reduced scale prototype with a Bluetooth-operated smart car. The car is a mock up of a train and incorporates sensors to mimic real-world safety environments, allowing a testable and controlled environment to validate the system. For the drowsiness detection of the loco pilot different libraries such as Dlib, scipy are used [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The RailGuard system is equipped with an ultrasonic sensor for obstacle detection [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], an IR sensor for track fault identification [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and a machine learning-based drowsiness detection module that uses Eye Aspect Ratio (EAR) from real-time camera input to determine the alertness of the operator. The system is supported by GSM and GPS modules to send SMS alerts and location data to railway authorities in case of any detected anomalies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExperimental outcomes show that the system provides an accuracy of 98.8% in obstacle detection, and the drowsiness detection module successfully detects fatigue with high accuracy. The track fault detection unit helps in early detection of faults, thus reducing the risk of derailment and the cost of maintenance. Combined, these features render RailGuard a solid and scalable solution for current railway safety requirements. Future enhancement can be enhanced predictive analytics and improved adaptability to diverse real-world conditions.\u003c/p\u003e"},{"header":"2. System Architecture","content":"\u003cp\u003eThis section describes the systematic approach used in the design, development, and testing of a miniature obstacle detection and automatic stopping system for trains. The methodology includes hardware setup, software development, sensor calibration, and motor control implementation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eA.\u0026nbsp;\u0026nbsp; \u003cstrong\u003eDrowsiness Detection Module\u0026nbsp; \u003c/strong\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInput Device: Camera\u003c/li\u003e\n\u003cli\u003eProcess Flow:\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eExtracting Frame: The camera captures live video feed and extracts frames.\u003c/li\u003e\n\u003cli\u003eFace Detection: Detects the presence of the driver\u0026rsquo;s face.\u003c/li\u003e\n\u003cli\u003eEye Detection: Identifies whether the driver's eyes are open or closed.\u003c/li\u003e\n\u003cli\u003eDrowsiness Check: If the eyes remain closed for a certain duration, the system concludes the driver is drowsy.\u003c/li\u003e\n\u003cli\u003eDecision: If drowsy (Yes), sends a signal to the Alert system.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eB.\u0026nbsp;\u0026nbsp; \u003cstrong\u003eObstacle Detection Module (Middle Section) \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInput Device: Ultrasonic Sensor\u003c/li\u003e\n\u003cli\u003eProcess Flow:\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eObstacle Detected?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf No: No further action is taken.\u003c/p\u003e\n\u003cp\u003eIf Yes: The system takes immediate action.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOnce detected, stops moving forward.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eC.\u0026nbsp;\u0026nbsp; \u003cstrong\u003eTrack Fault Detection Module (Right Section) \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInput Device: IR Sensor\u003c/li\u003e\n\u003cli\u003eProcess Flow:\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eSends sensor readings to the Track Fault Detector.\u003c/li\u003e\n\u003cli\u003eIf a fault is detected in the track, stops moving forward.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eD.\u0026nbsp;\u0026nbsp; \u003cstrong\u003eAlter System(Bottom Section)\u003c/strong\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCentral Component: Alert Module\u003c/li\u003e\n\u003cli\u003eFunction: Collects inputs from all three detection systems.\u003c/li\u003e\n\u003cli\u003eOutput Actions:\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eAudio Alert: Sounds an alarm to alert the driver.\u003c/li\u003e\n\u003cli\u003eMsg Alert: Sends a message notification (likely to admin or monitoring center) for remote awareness and action.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe core idea is to simulate a train equipped with obstacle detection and braking functionality using a small vehicle. The vehicle acts as a train prototype. It is fitted with an ultrasonic sensor at the front, which continuously scans the track ahead for any object. The Arduino Uno microcontroller serves as the brain of the system, processing sensor input and controlling the motor driver based on the detection logic. This model ensures real-time monitoring of the driver\u0026rsquo;s alertness, obstacles in the path, and track integrity. If any abnormality is detected in any of the three areas, an immediate alert is generated to prevent accidents and ensure passenger safety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Components Used in the Model\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"311\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr. No.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of Component\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQty.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription of\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse in Project\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eBreadboard 840 point with Jumper Wires Set\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1 set\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eSolderless board with 840\u0026nbsp;\u003c/p\u003e\n \u003cp\u003etie-points used for prototyping without soldering.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eUsed to build and test the electronic circuits of the train model.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003e18650 Battery Holder with Wires\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1 set\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003ePlastic holder for two 3.7V 18650 Li-ion batteries with wire leads.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eSupplies power to the train model and sensor modules.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eSPST Rocker Switch\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1 pack\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eSingle pole single throw\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eON/OFF switch with snapfit mounting.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eTurns ON/OFF the entire circuit manually for safety and control.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eHC-05 Bluetooth Module\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eBluetooth module for\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ewireless serial communication.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eUsed to send obstacle detection alerts via smartphone.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eL293D Motor Driver Shield\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eMotor driver shield compatible with Arduino to control DC motors.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eControls the motion of the miniature train wheels using BO motors.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eBO Dual Shaft Motors with Wheels\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1 pack\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eDC geared motors with wheels for robotics mobility.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eUsed to move the train model on the track layout.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eJumper Wires\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1 set\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eFlexible wires with M-M, M-F, F-F connectors for circuit connections.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eConnect all electronic components to the Arduino and breadboard.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eNEO-6M GPS Module\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eGPS receiver for real-time location tracking.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eMonitors train location for simulation of GPS tracking in railways.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eBMP180 Pressure Sensor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eSensor for atmospheric pressure and temperature readings.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eUsed to simulate weather condition awareness for train safety.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eIR Sensors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eReflective IR sensor module to detect nearby objects.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eDetects obstacles on the tracks and stops the train accordingly.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eDHT11 Sensor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eSensor to measure temperature and relative humidity.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eUsed to monitor environmental conditions near the railway track.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29487%;\"\u003e\n \u003cp\u003e12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7949%;\"\u003e\n \u003cp\u003eSIM900L GSM Module\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6282%;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9231%;\"\u003e\n \u003cp\u003eGSM communication module that sends and receives SMS and calls using a SIM card.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.359%;\"\u003e\n \u003cp\u003eSends SMS alerts to the admin when an obstacle or track fault is detected.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eComponent Selection and Integration\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe major components used in the system and their roles are as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eArduino Uno: Handles data processing and motor control logic.\u003c/li\u003e\n\u003cli\u003eHC-SR04 Ultrasonic Sensor: Measures the distance between the vehicle and obstacles.\u003c/li\u003e\n\u003cli\u003eL298N Motor Driver: Interfaces between the Arduino and DC motors, controlling direction and speed.\u003c/li\u003e\n\u003cli\u003eDC Motors: Propel the miniature train model forward.\u003c/li\u003e\n\u003cli\u003eHC-05 Bluetooth Module (optional): Sends real-time notifications to a mobile device.\u003c/li\u003e\n\u003cli\u003ePower Supply: A 9V battery or external adapter supplies power to the entire system.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll components were assembled on a mini train chassis. The sensor was mounted securely in front of the vehicle to ensure a clear detection path.\u003c/p\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eSensor Operation and Calibration \u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe HC-SR04 ultrasonic sensor emits ultrasonic waves through its transmitter. When these waves encounter an obstacle, they reflect back and are received by the sensor\u0026rsquo;s receiver.[3][4] The time delay between transmission and reception is measured by the Arduino, and the distance to the obstacle is calculated using:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Distance (in cm) = (Time \u0026times; Speed of Sound) / 2\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Where speed of sound is approximately 343 m/s in air.\u003c/p\u003e\n\u003cp\u003eTo ensure accuracy, initial tests were conducted to calibrate the sensor by comparing known distances with measured distances. A threshold distance of 30 cm was chosen as the critical range to trigger the stop condition.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eMotor Control Logic \u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe L298N motor driver is connected to the Arduino and controls the two DC motors based on the logic coded into the Arduino. The motor control pins are set HIGH or LOW to move the train forward. If the Arduino detects that an obstacle is within the threshold range, it instantly sends LOW signals to all motor pins, stopping the vehicle.\u003c/p\u003e\n\u003cp\u003eMotor operation conditions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf Distance \u0026gt; 30 cm \u0026rarr; Motors run (Train moves forward)\u003c/li\u003e\n\u003cli\u003eIf Distance \u0026le; 30 cm \u0026rarr; Motors stop (Train halts)\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCommunication Module (Optional) \u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo enhance system interactivity, an HC-05 Bluetooth module is included. When the obstacle is detected and the train stops, a predefined message is sent to a paired mobile device. This can be read via a Bluetooth terminal app, simulating real-time alerting or control system behavior in real-world applications.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eSoftware Development\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Arduino was programmed using the Arduino IDE in C/C++. The program initializes the sensor and motor pins, continuously monitors distance via the ultrasonic sensor, and applies decision-making logic for motion control. A loop structure ensures the train continuously checks for obstacles and responds instantly.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eTesting and Validation\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe system underwent multiple rounds of testing to verify consistent behavior under different conditions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVarying obstacle distances\u003c/li\u003e\n\u003cli\u003eDifferent object sizes and materials\u003c/li\u003e\n\u003cli\u003eContinuous motion and abrupt stopping\u003c/li\u003e\n\u003cli\u003eMobile notification (if Bluetooth is used)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe model performed as expected in controlled indoor environments, stopping the train within safe distances and preventing collision with placed objects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig 3 and fig 4 shows a circuit diagram and hardware implementation of smart robotic car using an Arduino Uno with an L293D motor driver shield to control four DC motors connected to M1\u0026ndash;M4 terminals. Power is supplied by two 18650 batteries connected to M+ and GND. The car is wirelessly controlled via an HC-05 Bluetooth module on TX/RX pins 0 and 1. An ultrasonic sensor on A0 and A1 detects obstacles and stops the car when objects detected infront of car [4]. An IR sensor on A3 detects surface faults like track gaps or table edges, triggering a stop as well. If an obstacle or fault is detected, an alert is sent to the admin via SMS [9].\u0026nbsp; A NEO-6M GPS module is connected to pins 7 and 8 to track the car's location in real time [6]. All modules share a common ground, and a rocker switch controls the entire system power. This setup ensures safe, smart, and responsive movement with real-time monitoring and alerts.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe proposed railway safety system, RailGuard, is implemented on a smart vehicle prototype designed to simulate a real-time railway environment. The car integrates multiple safety modules such as drowsiness detection, obstacle detection, and track fault detection using IR sensors. Each module works independently while communicating with a central alert system to trigger necessary actions, including vehicle stoppage and alert transmission via GSM. Below is the implementation breakdown of each major module:\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Drowsiness Detection Module\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe drowsiness detection module is deployed through a real-time camera feed and a trained machine learning model that is able to detect closed and open eye states. The camera keeps on capturing frames continuously, and face detection algorithms detect the region of the driver's face. In the detected face, the eyes are localized and an Eye Aspect Ratio (EAR) is computed. When the EAR goes below a set value for a particular range of consecutive frames, it points to the probability of closed eyes and, consequently, drowsiness. The alert signal gets sent to the alert system in this case. The method does not register false positives for the occurrence of blinks to identify accurate drowsiness and initiate an instant response.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePseudocode\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eStart camera Loop:\u003c/p\u003e \u003cp\u003eCapture video frame\u003c/p\u003e \u003cp\u003eDetect face in frame\u003c/p\u003e \u003cp\u003eDetect eyes\u003c/p\u003e \u003cp\u003eCalculate EAR\u003c/p\u003e \u003cp\u003eIf EAR\u0026thinsp;\u0026lt;\u0026thinsp;threshold for consecutive frames:\u003c/p\u003e \u003cp\u003eDisplay \u0026ldquo;Drowsy\u0026rdquo;\u003c/p\u003e \u003cp\u003eTrigger alert system\u003c/p\u003e \u003cp\u003eElse:\u003c/p\u003e \u003cp\u003eContinue monitoring\u003c/p\u003e \u003cp\u003eEnd loop\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Obstacle Detection Module\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe obstacle detection module employs an ultrasonic sensor (HC-SR04) attached to the car front to constantly scan the forward area. It sends an ultrasonic pulse and waits for its echo, calculating the time taken by the echo to come back. The time is utilized to determine the distance of any object in front of the car. If the measured distance is less than a critical threshold, e.g., 15 cm, then the system infers that there is an obstacle perilously near. The Arduino responds by putting all motor activities to a standstill, basically bringing the car to a stop.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] This keeps the car from crashing into surprise objects on the road, thus improving the security of the prototype.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePseudocode\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eLoop:\u003c/p\u003e \u003cp\u003eTrigger ultrasonic sensor\u003c/p\u003e \u003cp\u003eRead distance\u003c/p\u003e \u003cp\u003eIf distance\u0026thinsp;\u0026lt;\u0026thinsp;threshold:\u003c/p\u003e \u003cp\u003eStop the car\u003c/p\u003e \u003cp\u003eDisplay \"Obstacle Detected\"\u003c/p\u003e \u003cp\u003eTrigger alert system\u003c/p\u003e \u003cp\u003eElse:\u003c/p\u003e \u003cp\u003eContinue moving\u003c/p\u003e \u003cp\u003eEnd loop\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Track Fault Detection Module (IR Sensor)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe track fault detection module uses an infrared (IR) sensor, usually placed underneath the car. The IR sensor senses the existence or nonexistence of a surface below it based on the reflected IR signal. If the sensor fails to detect a reflective surface\u0026mdash;like the edge of a table or an opening in a track\u0026mdash;it reports a possible track fault. The system acts at once by stopping the motors so that the car does not fall or proceed on a defective course. This module is particularly convenient for simulating broken rails or absent track pieces in a model railway system.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePseudocode\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eLoop:\u003c/p\u003e \u003cp\u003eRead IR sensor output\u003c/p\u003e \u003cp\u003eIf surface not detected:\u003c/p\u003e \u003cp\u003eStop the car\u003c/p\u003e \u003cp\u003eDisplay \"Track Fault Detected\"\u003c/p\u003e \u003cp\u003eTrigger alert system\u003c/p\u003e \u003cp\u003eElse:\u003c/p\u003e \u003cp\u003eContinue moving\u003c/p\u003e \u003cp\u003eEnd loop\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Alert System Module\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe alert system is a centralized block that accumulates the signals from the drowsiness detection, obstacle detection, and track fault detection blocks. If any one of these subsystems detects a critical fault, the alert system either triggers a buzzer for local instant warnings or sends an SMS to a remote admin through the GSM module. For drowsiness, an eye closure alert is triggered; in the event of a track fault or obstacle, the vehicle stops and a message is sent. This provides fast awareness and reaction to prevent dangers, with real-time safety alerts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePseudocode\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eIf any alert received (Obstacle / Drowsiness / Track Fault):\u003c/p\u003e \u003cp\u003eActivate buzzer\u003c/p\u003e \u003cp\u003eSend SMS via GSM with message type and location (via GPS)\u003c/p\u003e \u003cp\u003eWait for acknowledgment or resume signal Else:\u003c/p\u003e \u003cp\u003eSystem idle\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe prototype of the obstacle detection and automatic stopping system was thoroughly tested under various conditions to evaluate its accuracy, responsiveness, and reliability. The tests were conducted in a controlled environment using different obstacle materials, distances, and angles.\u003c/p\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Test Setup\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eEnvironment: Indoor flat surface with adequate lighting\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDistance Threshold for Detection: 30 cm\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePower Supply: 9V battery\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMicrocontroller: Arduino Uno\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSensor: HC-SR04 Ultrasonic Sensor\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMotors: 2 DC geared motors\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eOptional Module: HC-05 Bluetooth for SMS notification\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable 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 \u003cp\u003ePerformance Evaluation of Obstacle Detection Module\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003cp\u003eCase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObstacle Material\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistance from Train\u003c/p\u003e\n \u003cp\u003e(cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpected\u003c/p\u003e\n \u003cp\u003eBehavior\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObserved Behavior\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardboard box\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlastic bottle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman hand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain\u003c/p\u003e\n \u003cp\u003eshould move\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain moved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetal rod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo obstacle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should move\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain moved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransparent glass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlight delay in stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial\u003c/p\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTilted object (angled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFabric cloth (soft object)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo stop detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFail\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReflective surface\u003c/p\u003e\n \u003cp\u003e(mirror)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStop after multiple detections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial\u003c/p\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaper (very light object)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo detection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFail\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple small objects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoving object (hand wave)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should ignore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIgnored properly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRock (irregular shape)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRain droplets (test weather)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should move\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain moved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFoggy conditions\u003c/p\u003e\n \u003cp\u003e(simulated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain should stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlight detection delay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial\u003c/p\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable No. 2 provides an overall performance analysis of the obstacle detection system built into the prototype train model. Every test case mimics a distinct real-life situation using a range of different obstacle materials, shapes, and environmental conditions. The primary objective of these tests is to test the system\u0026apos;s capability to identify obstacles accurately and react accordingly\u0026mdash;typically by bringing the train to a halt to avoid a collision.\u003c/p\u003e\n \u003cp\u003eMost of the test cases, including TC1 (Cardboard box), TC2 (Plastic bottle), TC4 (Metal rod), and TC7 (Tilted object), show the system\u0026apos;s consistent performance in halting when an obstacle is detected within the specified range, classifying these cases as Pass. These outcomes validate the consistency of the ultrasonic sensor under normal conditions and typical object types. Examples such as TC6 (Transparent glass) and TC9 (Reflective surface) ended in Partial Pass status due to delay in detection. These results point out possible areas of\u003c/p\u003e\n \u003cp\u003eweakness in the detection of materials that abnormally refract or reflect ultrasonic waves and indicate future room for improvement.\u003c/p\u003e\n \u003cp\u003eInterestingly, TC8 (Fabric cloth) and TC10 (Paper) were labeled as Fail, since the train failed to stop when it was supposed to. These observations indicate that very soft or extremely light objects have the potential to absorb sound waves instead of reflecting them, which makes them harder to detect. This observation can be useful to improve the sensitivity and signal processing algorithm of the sensor system.Other significant entries are TC12 (Moving object - hand wave) and TC14 (Rain droplets), where the system was supposed to disregard transient or irrelevant inputs. Both instances yielded a Pass, which indicates the system\u0026apos;s resilience in eliminating noise and only allowing legitimate threats to cause a response.TC15 (Foggy conditions) evaluated environmental interference and received a Partial Pass because of delay in detection, indicating towards improved performance in atmospheric disturbances.\u003c/p\u003e\n \u003cp\u003eIn summary, Table No. 2 confirms that the obstacle detection module is significantly effective in controlled and semidynamic scenarios. Nevertheless, the findings of the Partial Pass and Fail scenarios offer significant guidelines for enhancing detection algorithms and sensor calibration, especially for edge cases of soft materials or weather anomalies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Bluetooth Notification Test\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObstacle Detection\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSerial monitor Message\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDelay (seconds)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Obstacle detected- Train stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Message\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Observations\u003c/h2\u003e\n \u003cp\u003e\u0026bull; The system reliably detected most common objects and stopped the train within a 1\u0026ndash;2 cm tolerance of the threshold.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Detection was less accurate with transparent surfaces like glass due to weak ultrasonic reflection.\u003c/p\u003e\n \u003cp\u003e\u0026bull; The train responded within 200\u0026ndash;300 milliseconds of detecting an obstacle.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Bluetooth message delivery worked consistently within 5\u0026ndash;8 meters range.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ch4\u003e\u003cstrong\u003eTable 3. Drowsiness Detection Test Cases\u0026nbsp;\u003c/strong\u003e\u003c/h4\u003e\u0026nbsp;\u003cbr\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1749887129.png\"\u003e\n \u003c/div\u003e\n \u003cp\u003eTable No. 3 presents the experimental evaluation of the Drowsiness Detection Module using machine learning, which scans real-time video frames for signs of sleepiness. In Test 1, the system correctly recognized drowsiness in normal conditions, with eyes closed more than 3 seconds. Test 2 confirms that the system is able to correctly prevent false positives if the eyes are opened and looking alert. In Test 3, the module was tested for its ability to recognize drowsiness in subjects wearing glasses. The positive test verifies that the model works well even with obstructions on the face. Test 4 shows that the system continues to be accurate even when the face is as far as 4 meters from the camera, highlighting the resilience of both the facial landmark detection and classification algorithm.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003cstrong\u003eTable 4. Obstacle Detection Test Cases\u0026nbsp;\u003c/strong\u003e\u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img174988712970.png\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates obstacle detection Test Cases, It provides information about the results of several tests for obstacle detection. The table includes columns for test number, image, and result. Each row shows the results of a specific test, indicating whether or not an obstacle was detected in the image and what actions the system takes, if so, the type of obstacle and its detection accuracy.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1749887126.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable. 5 illustrates Fault detection Test Cases, It provides information about the results of several tests for Fault detection. The table includes columns for test number, image, and result. Each row shows the results of a specific test, indicating not an fault was detected in the image and, if so, the type of Fault and its detection accuracy.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModule-Wise Performance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModule\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInput Device\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFunctionality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse Time\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerformance Insight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrawsiness Detection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCamera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetects drowsiness using Eye Aspect Ratio (EAR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffectively identifies drowsiness, even with spectacles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObstacle Detection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUltrasonic Sensor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetects nearby obstacles and halts the car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResponds quickly to various object types and distances\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTack Fault Detection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR Sensor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetects gaps/cracks simulating broken track\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccurate in identifying missing surface or track anomalies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlert \u0026amp; Notification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSM Module\u0026thinsp;+\u0026thinsp;Buzzer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSends SMS alert and sounds buzzer upon detection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelivers real-time alerts to admin with GPS integration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e gives a complete picture of the performance of every module incorporated in the RailGuard Smart Train Safety System. All the modules were rigorously tested in controlled environments to ascertain reliability, precision, and prompt responseparticularly important in real-time railway settings. The Drowsiness Detection Module, which was implemented using computer vision and facial landmark detection, effectively identified closed-eye conditions from a distance, providing early warning before fatigue accidents happen. The Obstacle Detection Module proved good performance in bringing the vehicle to a halt at all distances and angles when obstacles were found, demonstrating powerful real-time scanning performance. In the Track Fault Detection Module, the IR sensor effectively detected loss of surface or discontinuities in simulated tracks (e.g., table boundaries), halting the train from causing derailment conditions. The Alert and Notification System safely delivered SMS warnings via the GSM module and triggered the buzzer to alert surrounding staff, reducing response times in crisis situations.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, presents a comparative assessment of the four fundamental modules integrated into the proposed IoT based rail safety framework: Drowsiness Detection, Obstacle Detection, Track Fault Detection, and Alert \u0026amp; Notification.\u003c/p\u003e\n \u003cp\u003eEach module is analyzed with respect to two vital performance factors\u0026mdash;Accuracy (%) and Response Time (ms). The Drowsiness Detection module, employing a camera and Eye Aspect Ratio (EAR) approach, has an impressive 98.5% accuracy and 150 ms response time. Having the maximum response time of all modules, it is reasonable given the intensive image processing and machine learning processes employed in identifying eye movement and levels of alertness. The Obstacle Detection module, which is driven by an ultrasonic sensor, is most accurate at 98.8% and responds very fast with a response time of 100 ms and is thus extremely efficient in detecting obstacles at different distances and stopping the system immediately. The Track Fault Detection module uses an IR sensor to identify surface irregularities such as gaps or cracks on the rail track. It has 98.3% accuracy and a response time of 120 ms, making it a reliable means of real-time physical track fault detection. Finally, the Alert \u0026amp; Notification module, incorporating a GSM module and buzzer, provides the quickest response time of 80 ms and highest accuracy of 99.1%. It provides instant alerts to administrators or monitoring centers with GPS location information, improving remote awareness and decision-making.\u003c/p\u003e\n \u003cp\u003eIn general, the graph indicates the system\u0026apos;s ever-high accuracy (over 98%) as well as real-time responsiveness, validating its credibility and appropriateness for railway safety scenarios in which prompt decisions and high accuracy matter.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe concept proposed RailGuard Smart Train Safety System is a global model built with the idea to mimic railway security through the applications of embedded systems and Internet-of-Things technology. In assembling several modules for detection, for example, sleepiness tracking, obstacle evasion, track failure observation, and alerting over the GSM mode\u0026mdash;multiple fatal danger factors causing railways accidents are eliminated.\u003c/p\u003e \u003cp\u003eUnlike traditional railway safety systems based on sporadic manual checks and response time-dependent human action, this system guarantees real-time observation and automatic response. For example, the computer vision and machine learning-based drowsiness detection module ensures high accuracy (98.5%) in recognizing fatigue by utilizing the Eye Aspect Ratio (EAR) method. It is consistent even in changing lighting situations and when the driver is wearing glasses.\u003c/p\u003e \u003cp\u003eThe obstacle detection module, centered on an ultrasonic sensor, repeatedly stops the train whenever obstacles are found to be within 15 cm distance, reducing chances of collision. Simultaneously, the track fault detection module correctly detects the surface discontinuity with IR sensors by efficiently modeling broken or cracked rails. Adding a GSM module for alerts helps in conveying any unusual condition\u0026mdash;whether fatigue, obstacle, or fault\u0026mdash;to a distant monitoring agency immediately.\u003c/p\u003e \u003cp\u003eInterestingly, the prototype is tested with a Bluetooth-controlled robot car that simulates train operations in a reduced-scale setting. In spite of the simplification, the test results mirror closely actual operation and demonstrate very high-reliability safety. With less than 150 ms of response time and system accuracy over 98%, this project proves the feasibility of smart sensing technology in enhancing rail safety.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that it is evident that the accuracy and performance of the IoT-based system significantly improve over time as the modules synchronize and adapt. While the conventional system plateaus at around 66% accuracy, the proposed system reaches up to 95% accuracy due to better responsiveness and automation. This clear margin illustrates how modern embedded and wireless communication technologies can enhance railway safety and prevent accidents at an early stage.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe proposed IoT-based railway safety system offers a smart, affordable, and automated solution to reducing train accidents. It easily combines various modules such as obstacle detection through ultrasonic sensors, track fault detection through IR sensors, real-time GPS tracking, and GSM-based alerting to facilitate early detection of threats and prompt communication with authorities. An added feature in this system is the integration of a drowsiness detection module with machine learning that tracks the loco pilot's eye movement to detect signs of fatigue\u0026mdash;a prime factor that usual systems tend to neglect.\u003c/p\u003e \u003cp\u003eUnlike conventional railway safety mechanisms that rely extensively on late human intervention and manual checks, this system involves constant, automatic, real-time monitoring and instantaneous response to anomalous conditions. Experimental observations indicate that the system detects obstacles with accuracy 98.8%, track faults with accuracy 97.9%, and drowsiness with accuracy 98.5%. These observations indicate a notable improvement over traditional methods in terms of precision as well as responsiveness. Deployment of these intelligent modules in a Bluetoothoperated prototype car efficiently proves the applicability of the system in practical railway applications. In general, the system presented has a powerful and scalable solution for improving railway transport safety and reliability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical Trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.P. (Mrunal Pathak), D.K. (Dnyaneshwari Kondhalkar), S.P. (Sejal Patil), R.D. (Rutuja Domale), and S.W. (Swarangi Waikar) all contributed equally to the conception and design of the project, data collection and analysis, hardware and software development, system testing and validation, literature review, and manuscript preparation. All authors reviewed and approved the final manuscript \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were used or generated. The system operates in real-time using sensor inputs and live face data from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all authors participating in the study, including those involved in real-time face data collection for drowsiness detection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have provided informed consent for the use and publication of their own face images in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were conducted in accordance with institutional ethical guidelines. The research protocol was approved by the \u003cem\u003eInstitutional Ethics Committee of All India Shri Shivaji Memorial Society\u0026rsquo;s Institute of Information Technology, Pune\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHarish Kumar, N Deepak, G Nagaraja J, \u0026ldquo;An IoT based Obstacle Detection and Alerting System in Vehicles using Ultrasonic Sensor\u0026rdquo; Special Issue \u0026ndash;\u003c/li\u003e\n \u003cli\u003eSreenath C , Jishnu V J , Nithin N , Jisha K V, \u0026ldquo;Microcontroller based track crack detection\u0026rdquo; Vol (5), Issue (7), July. 2024.\u003c/li\u003e\n \u003cli\u003eFarooq, Muhammad Siddique, Imran Shafi, Harris Khan, Isabel De La Torre D\u0026iacute;ez, Jose Bre\u0026ntilde;osa, Julio C\u0026eacute;sar Mart\u0026iacute;nez Espinosa, and Imran Ashraf. 2022. \u0026quot;IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition\u0026quot; \u003cem\u003eSensors\u003c/em\u003e 22, no. 22: 8914. https://doi.org/10.3390/s22228914\u003c/li\u003e\n \u003cli\u003eLeong and R. K. Ramasamy, \u0026quot;Obstacle Detection and Distance Estimation for Visually Impaired People,\u0026quot; in IEEE Access, vol. 11, pp. 136609-136629, 2023, doi: 10.1109/ACCESS.2023.3338154.\u003c/li\u003e\n \u003cli\u003eMohamad, K. A. ., Aziz, A. A. ., \u0026amp; Alias, A. . (2020). Obstacle Detection System for Railways using IoT Sensors . \u003cem\u003eEvolution in Electrical and Electronic Engineering\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 57-63.\u003c/li\u003e\n \u003cli\u003eU. R. Siddiqui, A. A. Saleem, M. A. Raza, K. Zafar, K. Munir and S. Dudley, \u0026quot;IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis,\u0026quot; in IEEE Access, vol. 10, pp. 106520-106533, 2022, doi: 10.1109/ACCESS.2022.3210326.\u003c/li\u003e\n \u003cli\u003eA. Shah, N. A. Bhatti, K. Dev and B. S. Chowdhry, \u0026quot;MUHAFIZ: IoT-Based Track Recording Vehicle for the Damage Analysis of the Railway Track,\u0026quot; in \u003cem\u003eIEEE Internet of Things Journal\u003c/em\u003e, vol. 8, no. 11, pp. 9397-9406, 1 June1, 2021, doi: 10.1109/JIOT.2021.3057835.\u003c/li\u003e\n \u003cli\u003eThinakaran, S. Jalari, V. Neerugatti, M. R. Nalluri, S. Chukka and R. R. Cholla, \u0026quot;Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring,\u0026quot; \u003cem\u003e2024 9th International Conference on Information Technology and Digital Applications (ICITDA)\u003c/em\u003e, Nilai, Negeri Sembilan, Malaysia, 2024, pp. 01-04, doi: 10.1109/ICITDA64560.2024.10809610.\u003c/li\u003e\n \u003cli\u003eDevulapalli, A. V. Vennelakanti and R. Sallakunta, \u0026quot;Real-Time Railway Track Fault Detection and Environmental Monitoring System Using Arduino and GSM,\u0026quot; \u003cem\u003e2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)\u003c/em\u003e, Tashkent, Uzbekistan, 2024, pp. 1410-1415, doi: 10.1109/ICTACS62700.2024.10840621.\u003c/li\u003e\n \u003cli\u003eMohanty, S. V. Hegde, S. Prasad and J. Manikandan, \u0026quot;Design of Real-time Drowsiness Detection System using Dlib,\u0026quot; \u003cem\u003e2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)\u003c/em\u003e, Bangalore, India, 2019, pp. 1-4, doi: 10.1109/WIECON-ECE48653.2019.9019910.\u003c/li\u003e\n \u003cli\u003eE. Boussaki, R. Latif and A. Saddik, \u0026quot;Drowsiness detection using Dlib: an overview,\u0026quot; \u003cem\u003e2023 7th IEEE Congress on Information Science and Technology (CiSt)\u003c/em\u003e, Agadir - Essaouira, Morocco, 2023, pp. 150-154, doi: 10.1109/CiSt56084.2023.10409980.\u003c/li\u003e\n \u003cli\u003eA. Noor Reza, E. A. Zaki Hamidi, N. Ismail, M. R. Effendi, E. Mulyana and W. Shalannanda, \u0026quot;Design a Landmark Facial-Based Drowsiness Detection Using Dlib And Opencv For Four-Wheeled Vehicle Drivers,\u0026quot; \u003cem\u003e2021 15th International Conference on Telecommunication Systems, Services, and Applications (TSSA)\u003c/em\u003e, Bali, Indonesia, 2021, pp. 1-5, doi: 10.1109/TSSA52866.2021.9768278.\u003c/li\u003e\n \u003cli\u003eSingh, S. P. S. Chauhan and E. Rajesh, \u0026quot;Real-Time Driver Drowsiness Detection Using Dlib And openCV,\u0026quot; \u003cem\u003e2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)\u003c/em\u003e, Greater Noida, India, 2022, pp. 956-960, doi: 10.1109/ICAC3N56670.2022.10074245.\u003c/li\u003e\n \u003cli\u003eBharavi and R. M. Sukesh, \u0026quot;Design and development of GSM and GPS tracking module,\u0026quot; \u003cem\u003e2017 2nd IEEE International Conference on Recent Trends in Electronics, Information \u0026amp; Communication Technology (RTEICT)\u003c/em\u003e, Bangalore, India, 2017, pp. 283-288, doi: 10.1109/RTEICT.2017.8256602.\u003c/li\u003e\n \u003cli\u003eKokane, Rishabh Jagtap, Yash Nandre, Tanay Kadam \u0026ldquo;IOT Based Trash Collector Boat Sachin\u0026rdquo; in I International Journal of Technology Engineering Arts Mathematics Science Vol. 2, No. 1, June2022, pp. 09~13\u003c/li\u003e\n \u003cli\u003eD. Koffman, B. C. Waltrip, and Y. Wang, \u0026lsquo;\u0026lsquo;Eddy current rail inspection using AC bridge techniques,\u0026rsquo;\u0026rsquo; J. Res. Nat. Inst. Standards Technol., vol. 118, pp. 140\u0026ndash;149, Feb. 2013.\u003c/li\u003e\n \u003cli\u003e-K. Shin, D.-M. Choi, Y.-J. Kim, and S.-S. Lee, \u0026lsquo;\u0026lsquo;Signal characteristics of differential-pulsed eddy current sensors in the evaluation of plate thickness,\u0026rsquo;\u0026rsquo; NDT E Int., vol. 42, no. 3, pp. 215\u0026ndash;221, Apr. 2009.\u003c/li\u003e\n \u003cli\u003eWu, Y. Yang, E. Li, Z. Deng, Y. Kang, C. Tang, and A. I. Sunny, \u0026lsquo;\u0026lsquo;A highsensitivity MFL method for tiny cracks in bearing rings,\u0026rsquo;\u0026rsquo; IEEE Trans. Magn., vol. 54, no. 6, pp. 1\u0026ndash;8, Jun. 2018.\u003c/li\u003e\n \u003cli\u003eShruthi, G. M. Iype, K. C. P. M. Sharon, and S. Subhash, \u0026lsquo;\u0026lsquo;Rail track defect detection using enhanced method of magnetic flux leakage signal,\u0026rsquo;\u0026rsquo; in Proc. Int. Conf. Design Innov. Compute Communicate Control (ICDIC), Jun. 2021, pp. 277\u0026ndash;280.\u003c/li\u003e\n \u003cli\u003eJia, S. Zhang, P. Wang, and K. Ji, \u0026lsquo;\u0026lsquo;A method for detecting surface defects in railhead by magnetic flux leakage,\u0026rsquo;\u0026rsquo; Appl. Sci., vol. 11, no. 20, p. 9489, Oct. 2021.\u003c/li\u003e\n \u003cli\u003eTsukada, Y. Majima, Y. Nakamura, T. Yasugi, N. Song, K. Sakai, and T. Kiwa, \u0026lsquo;\u0026lsquo;Detection of inner cracks in thick steel plates using unsaturated AC magnetic flux leakage testing with a magnetic resistance gradiometer,\u0026rsquo;\u0026rsquo; IEEE Trans. Magn., vol. 53, no. 11, pp. 1\u0026ndash;5, Nov. 2017.\u003c/li\u003e\n \u003cli\u003eK. Okolo and T. Meydan, \u0026lsquo;\u0026lsquo;Pulsed magnetic flux leakage method for hairline crack detection and characterization,\u0026rsquo;\u0026rsquo; AIP Adv., vol. 8, no. 4, Apr. 2018, Art. no. 047207.\u003c/li\u003e\n \u003cli\u003eBener, A., Yildirim, E., \u0026Ouml;zkan, T., Lajunen, T.: Driver sleepiness, fatigue, careless behavior and risk of motor vehicle crash and injury: population-based case and control study. J. Traffic Transp. Eng. (English Ed.) \u003cstrong\u003e4\u003c/strong\u003e(5), 496\u0026ndash; 502 (2017)\u003c/li\u003e\n \u003cli\u003eSandeep CH, Naresh Kumar S, and Pramod Kumar P 2018 Security challenges and issues of the IoT system. Indian J Public Health Res Dev 9 11 748-753.\u003c/li\u003e\n \u003cli\u003eN. Sree, C. V. Raj and R. Madhavan, \u0026quot;Obstacle avoidance for UAVs used in road accident monitoring,\u0026quot; \u003cem\u003e2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)\u003c/em\u003e, Kolkata, India, 2017, pp. 1-6, doi: 10.1109/IEMENTECH.2017.8077022.\u003c/li\u003e\n \u003cli\u003ePassarella, B. Tutuko, and A. P. P. Prasety \u0026quot;Design Concept of train obstacle detection system in Indonesia\u0026quot; IJRRAS, vol. 9, no. 3, 2011.\u003c/li\u003e\n \u003cli\u003eS. Punekar and A. A. Raut, \u0026quot;Improving railway safety with obstacle detection and tracking system using GPSGSM model,\u0026quot; International Journal of Scientific \u0026amp;Engineering Research, vol. 4, no. 8, 2013.\u003c/li\u003e\n \u003cli\u003eRamasamy, \u0026quot;Automatic obstacle detection in\u003c/li\u003e\n \u003cli\u003erailway network using embedded system \u0026quot; 2014, vol. 13.\u003c/li\u003e\n \u003cli\u003eK, \u0026quot;Detection and warning system for railway track using wireless with multi sensor,\u0026quot; International Journal of Research in Advent Technology, vol. 2, no. 5, pp. 2321\u0026ndash; 9637, 2014.\u003c/li\u003e\n \u003cli\u003eB. Q. Chowdhury, M. R. Khan and M. A. Razzak, \u0026quot;Automation of Rail Gate Control with Obstacle Detection and Real Time Tracking in the Development of Bangladesh Railway,\u0026quot; \u003cem\u003e2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC)\u003c/em\u003e, Kuching, Malaysia, 2020, pp. 1-6, doi: 10.1109/R10-HTC49770.2020.9356986.\u003c/li\u003e\n \u003cli\u003eD. N, S. V, S. L, S. G and P. M. R, \u0026quot;An IoT Framework for Effective Railway Looping and Obstacles Detection,\u0026quot; \u003cem\u003e2024 International Conference on Inventive Computation Technologies (ICICT)\u003c/em\u003e, Lalitpur, Nepal, 2024, pp. 1814-1818, doi: 10.1109/ICICT60155.2024.10544410.\u003c/li\u003e\n \u003cli\u003eAhamed, N. Islam, M. A. S. Soikot, M. S. Hossen, R. Ahmed and M. A. Hasan, \u0026quot;Train Collision Avoidance Using GPS and GSM Module,\u0026quot; \u003cem\u003e2019 International Conference on Power Electronics, Control and Automation (ICPECA)\u003c/em\u003e, New Delhi, India, 2019, pp. 1-4, doi: 10.1109/ICPECA47973.2019.8975543.\u003c/li\u003e\n \u003cli\u003eVemula, S. Dawn, A. Machagiri, S. L. Potipireddi and B. R. Bobbili, \u0026quot;Fault Detection in Railway Track using GSM And GPS System,\u0026quot; \u003cem\u003e2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)\u003c/em\u003e, Tirunelveli, India, 2023, pp. 259-264, doi: 10.1109/ICOEI56765.2023.10125887.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obstacle Detection, Fault Detection, Drowsiness Detection, Arduino Uno, Ultrasonic Sensor, Infrared Sensor, Train Safety System, Automatic Braking, Embedded System, Bluetooth Communication, Railway Automation, Smart Transportation","lastPublishedDoi":"10.21203/rs.3.rs-6742159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6742159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rising number of railway accidents caused by human drowsiness, track failures, and unforeseen barriers reflects the imperative need for an automated and smart safety system. This paper proposes an IoT-based Smart Railway Safety System to keep track of real-time conditions and provide secure train operations by integrating four major modules: Drowsiness Detection, Obstacle Detection, Track Fault Detection, and Alert \u0026amp; Notification System. Drowsiness Detection module uses computer vision and Eye Aspect Ratio (EAR) evaluation to determine the alertness of the loco pilot by measuring eye closure time through a live camera feed. Extended eye closure initiates instant alerts to avoid probable accidents. The Obstacle Detection system using an ultrasonic sensor constantly looks for unforeseen objects in the front region and stops the train when required. Track Fault Detection utilizes an infrared (IR) sensor to detect cracks or gaps on the railway track those mimic breakages, making early intervention possible. The Alert \u0026amp; Notification module encompasses a GSM and GPS system to send real-time SMS messages with geographic co-ordinates to concerned authorities, and an on-board buzzer alerts the driver. Performance testing illustrates the high reliability of the system, with response rates between 98.3% and 99.1% and response times of less than 150 milliseconds. Compared with conventional manually monitored systems, this solution presents an economical, proactive, and real-time method for railway safety. The combination of hardware-based sensing and software-based intelligence makes this system a promising model for contemporary railway accident avoidance and safety promotion.\u003c/p\u003e","manuscriptTitle":"Railguard: An IOT Powered Smart Railway Safety and Monitoring System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-14 07:55:56","doi":"10.21203/rs.3.rs-6742159/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6acc7af-6c07-4d04-a405-284bd2276eb0","owner":[],"postedDate":"June 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-08T08:08:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-14 07:55:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6742159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6742159","identity":"rs-6742159","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}