A Data-Driven Spatio-Temporal Video-Based Model for Pedestrian-Vehicle Risk Prediction

A Data-Driven Spatio-Temporal Video-Based Model for Pedestrian-Vehicle Risk Prediction | 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 A Data-Driven Spatio-Temporal Video-Based Model for Pedestrian-Vehicle Risk Prediction Oumaima BENKHADDA, Meriem MANDAR, Nédra MELLOULI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8743030/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 This work presents an approach to assessing the risk of interaction between a vehicle and a pedestrian, based on a combination of spatial, temporal, and dynamic information extracted from video images. Pedestrians are detected using a visual detection model, while the distance between them and the vehicle is estimated based on the geometry of a monocular camera. The speed of the vehicle is deduced from optical tracking based on the Lucas-Kanade algorithm. Using data from the World Health Organization on the severity of impacts, we propose a probabilistic formulation that allows us to jointly estimate the probability of collision and the potential severity of injuries. Unlike traditional indicators, which are limited to instantaneous measurements (speed, distance, or time to collision), our model takes into account the cumulative duration during which a pedestrian remains below a critical proximity threshold. This method of integrating temporal exposure provides a more accurate picture of the gradual evolution of danger. Experiments conducted on the JAAD dataset show that this approach allows for a more refined distinction between different levels of risk, particularly in urban situations at intermediate speeds where preventive actions remain possible. The results as a whole highlight the value of simultaneously integrating distance, speed, and exposure time for a more realistic and operational assessment of pedestrian-vehicle risk. Pedestrian Risk Assessment Vehicle-Pedestrian Distance Vehicle Speed Cumulative Exposure Time Lucas-Kanade Optical Flow. Full Text 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-8743030","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593252438,"identity":"22d86119-7d0e-4bc6-be04-68832460913a","order_by":0,"name":"Oumaima BENKHADDA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYLCCBCDiZ+9/+ADI5uEjWotkzxlmA5AWNqItMpjhwyYBYhHUwi+RnfjhYY5dnoEE77HKrzl2MmwMzA8f3cCjRXJG7maJxG3JxebSfWm3ZbclAx3GZmycg0eLwY3cDUAtzIk75xwwuy25jRmohYdNmoCWzT8St9UnbriRYFYsua2eKC3bgLYcBmrJMWP8uO0wYS2SPW+3WSRuO14s2XMsWZpx23EeNmYCfuFnz9188+e26jx+9uaDH4EMeyDj4WN8WlAAMw+YJFY5CDD+IEX1KBgFo2AUjBgAANL0S0O49P/eAAAAAElFTkSuQmCC","orcid":"","institution":"HASSAN II University","correspondingAuthor":true,"prefix":"","firstName":"Oumaima","middleName":"","lastName":"BENKHADDA","suffix":""},{"id":593252439,"identity":"03f1e15c-adfa-47d7-857e-b63acdf727c2","order_by":1,"name":"Meriem MANDAR","email":"","orcid":"","institution":"HASSAN II University","correspondingAuthor":false,"prefix":"","firstName":"Meriem","middleName":"","lastName":"MANDAR","suffix":""},{"id":593252440,"identity":"19b9a026-84fb-4871-a448-a14c3a9daedf","order_by":2,"name":"Nédra MELLOULI","email":"","orcid":"","institution":"Pôle Universitaire Léonard de Vinci","correspondingAuthor":false,"prefix":"","firstName":"Nédra","middleName":"","lastName":"MELLOULI","suffix":""}],"badges":[],"createdAt":"2026-01-30 16:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8743030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8743030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103050389,"identity":"dd05f808-7f1d-4f20-8665-6b6a6639eb6e","added_by":"auto","created_at":"2026-02-20 07:49:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1517174,"visible":true,"origin":"","legend":"","description":"","filename":"ADataDrivenSpatioTemporalVideoBasedModelforPedestrianVehicleRiskPrediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8743030/v1_covered_9c9a8e72-37e9-4cf5-be5b-331dc04099c7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Data-Driven Spatio-Temporal Video-Based Model for Pedestrian-Vehicle Risk Prediction ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"Pedestrian Risk Assessment, Vehicle-Pedestrian Distance, Vehicle Speed, Cumulative Exposure Time, Lucas-Kanade Optical Flow.","lastPublishedDoi":"10.21203/rs.3.rs-8743030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8743030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This work presents an approach to assessing the risk of interaction between a vehicle and a pedestrian, based on a combination of spatial, temporal, and dynamic information extracted from video images. Pedestrians are detected using a visual detection model, while the distance between them and the vehicle is estimated based on the geometry of a monocular camera. The speed of the vehicle is deduced from optical tracking based on the Lucas-Kanade algorithm. Using data from the World Health Organization on the severity of impacts, we propose a probabilistic formulation that allows us to jointly estimate the probability of collision and the potential severity of injuries. Unlike traditional indicators, which are limited to instantaneous measurements (speed, distance, or time to collision), our model takes into account the cumulative duration during which a pedestrian remains below a critical proximity threshold. This method of integrating temporal exposure provides a more accurate picture of the gradual evolution of danger. Experiments conducted on the JAAD dataset show that this approach allows for a more refined distinction between different levels of risk, particularly in urban situations at intermediate speeds where preventive actions remain possible. The results as a whole highlight the value of simultaneously integrating distance, speed, and exposure time for a more realistic and operational assessment of pedestrian-vehicle risk.","manuscriptTitle":"A Data-Driven Spatio-Temporal Video-Based Model for Pedestrian-Vehicle Risk Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 13:39:53","doi":"10.21203/rs.3.rs-8743030/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":"e472d5de-f945-4183-b30b-cfc620245747","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T13:39:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 13:39:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8743030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8743030","identity":"rs-8743030","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}