Computer Vision and Machine Learning Based Traffic Scene Perception and Car Driver Injury Assessment

Computer Vision and Machine Learning Based Traffic Scene Perception and Car Driver Injury Assessment | 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 Computer Vision and Machine Learning Based Traffic Scene Perception and Car Driver Injury Assessment Sun Wei, Alfian Bin Abdul Halin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3282217/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 Traffic scene perception and risk assessment for car drivers is critical to improving road safety. This study aimed to develop a deep supervised learning detector for automotive traffic scenes based on computer vision and machine learning techniques to detect the effects of weather factors and road factors on car drivers and to assess the degree of injury in traffic accidents. The goal of this research was to assess the extent to which motorists are injured in traffic accidents in each traffic scenario by analyzing and evaluating weather factors and road factors. This approach used deeply supervised learning detectors to detect and classify weather and road factors in motor vehicle traffic scenarios using computer vision and machine learning techniques. By processing and analyzing the data, it can determine the relationship between weather and road factors and the extent of injuries sustained by motorists. In addition, the research analyzed the relationship between weather and road factors and the degree of driver injury using a linear regression model. By calculating the regression coefficients, the research can quantify the effect of weather and road factors on the level of driver injury. The experimental results showed that weather and road factors were positively related to the level of injury of motorists. This study provided important insights into car driver risk assessment in traffic scenarios through the evaluation and discussion of deep supervised learning detectors and linear regression models. computer vision machine learning deep supervised learning car traffic scenes weather factors road factors traffic accidents injury levels linear regression models 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. 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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-3282217","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":228019180,"identity":"ce265455-b753-4c0f-be82-e8ce5dee87ac","order_by":0,"name":"Sun Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYFAC5oMPP/xgkwMxJSAiCYS0sCUbS/bwGTOwgbUYEKOFR02Ah00usYFoLfL+Z9gYJHjM0ufP7zG88XHHHwZ+9hwDZt4d2xh0Z2DXa3gj99iDAou03A3HeIwtZ54xYJDseQPUcuY2g9kNHFpm8KUbSPAcy93AxmMmzdtmwGBwA2RLGx4t/WfMJHjY/qfLtwG1/AVqsSekRZ4hB6SFLYHhGFALI8gWCQJaDCTSQIHMZrjhWFqxZW+bMY/EmWcFB+eeuc1jduYBdlv6D4OjUl6++fDGGz/b5OT425M3Pni747ac2XEcthxAE+ABEQcYG4AMARx+acAqzAgS5kc3bhSMglEwCkYoAACtVVzrfGDsGAAAAABJRU5ErkJggg==","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Wei","suffix":""},{"id":228019181,"identity":"dd8562cc-9563-4385-b359-d34dd4363cb5","order_by":1,"name":"Alfian Bin Abdul Halin","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alfian","middleName":"Bin Abdul","lastName":"Halin","suffix":""}],"badges":[],"createdAt":"2023-08-21 10:59:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3282217/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3282217/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":43649650,"identity":"99449c5b-e17c-46a7-8f72-2ed0acd7876b","added_by":"auto","created_at":"2023-09-25 17:37:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":743895,"visible":true,"origin":"","legend":"","description":"","filename":"ComputerVisionandMachineLearningBasedTrafficScenePerceptionandCarDriverInjuryAssessment.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3282217/v1_covered_658c9da6-40e3-40a7-b4b5-2559bbe482b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computer Vision and Machine Learning Based Traffic Scene Perception and Car Driver Injury Assessment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"computer vision, machine learning, deep supervised learning, car traffic scenes, weather factors, road factors, traffic accidents, injury levels, linear regression models","lastPublishedDoi":"10.21203/rs.3.rs-3282217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3282217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Traffic scene perception and risk assessment for car drivers is critical to improving road safety. 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