Deep Learning-based Visual Risk Warning System for Autonomous Driving

Deep Learning-based Visual Risk Warning System for Autonomous Driving | 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 Deep Learning-based Visual Risk Warning System for Autonomous Driving Chengqun Qiu, Hao Tang, Xixi Xu, Yu Peng, Jie Ji, Xinchen Ji, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4483213/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 In autonomous driving, the identification and tracking of multiple vehicles on the road are critical tasks. This paper aims to develop a risk warning system using deep learning algorithms to address the heterogeneous, high-dynamic, and complex driving environments. To enhance the generalization capability and detection accuracy of small objects in road perception, we propose a novel VBFNet-YOLOv8 algorithm for real-time vehicle identification, tracking, distance measurement, and speed estimation. Specifically, we replace the Backbone of the original YOLOv8 network with the VanillaNet structure and upgrade the traditional PANet in the neck part to Bi-FPN. By integrating the optimized YOLOv8n algorithm with Deepsort and TTC algorithms, we achieve a comprehensive road risk assessment. The algorithm continuously tracks the targets, and the TTC algorithm intuitively assesses the risk. Finally, the system provides layered warnings by changing the color of the bounding boxes, offering drivers an integrated and real-time risk alert. Comparative experimental results show that the optimized algorithm improves Precision by 0.61%, [email protected] by 0.63%, and [email protected] :0.95 by 0.70%. In the road tests on sections A and B, the detection frame rate of the risk warning system maintained a minimum of 37.1fps and a maximum of 56.4fps. The detection Confidence of various objects remained above 0.67, reaching up to 0.97. Risk warning Deep learning Machine vision YOLOv8 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. 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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-4483213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309402544,"identity":"894c9847-8a19-42a1-b2f0-3f9a9512fc65","order_by":0,"name":"Chengqun Qiu","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Chengqun","middleName":"","lastName":"Qiu","suffix":""},{"id":309402545,"identity":"6d8310e6-3a1f-4795-b3df-530087757ed7","order_by":1,"name":"Hao Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYDACCYYEIGnDw8/fQKQOHoiWNBnJGQeI1wICh20MGhKI1GIv3fD4M0/NeR4DhgOMHz7mEGOLzIE0aZ5jt3nMmRuYJWduI8phCWnMvA23eSwbDrAx8xKpJfkzb8M5HoMDCcRrSZDmbThAipYbCWmSc44l80jOONhMnF/YZ+Qkf3hTY2fPz9988MNHYrQA7Ulg4gEzGBuIUg+y5wDjD2LVjoJRMApGwcgEADiiMwD1xkWiAAAAAElFTkSuQmCC","orcid":"","institution":"Yancheng Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Tang","suffix":""},{"id":309402546,"identity":"86634cee-9fac-4a4c-8d49-713c556b06bf","order_by":2,"name":"Xixi Xu","email":"","orcid":"","institution":"Yancheng Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xixi","middleName":"","lastName":"Xu","suffix":""},{"id":309402547,"identity":"a8a27a0b-9fa0-4e14-8f96-be8836a1ab96","order_by":3,"name":"Yu Peng","email":"","orcid":"","institution":"Yancheng Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Peng","suffix":""},{"id":309402548,"identity":"413c1ccd-4027-4366-8178-4a1c482a9ed8","order_by":4,"name":"Jie Ji","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Ji","suffix":""},{"id":309402549,"identity":"022bd97a-3861-45ff-a8e0-ccaf925efa1e","order_by":5,"name":"Xinchen Ji","email":"","orcid":"","institution":"Yancheng Teachers University","correspondingAuthor":false,"prefix":"","firstName":"Xinchen","middleName":"","lastName":"Ji","suffix":""},{"id":309402550,"identity":"0e28d44b-41d4-46fc-93a8-f95dea12a38c","order_by":6,"name":"Shengqiang Lin","email":"","orcid":"","institution":"Yancheng Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shengqiang","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-05-27 07:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4483213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4483213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81267658,"identity":"dbaf610a-6aac-48bf-9b4d-b7fcb1bd35d1","added_by":"auto","created_at":"2025-04-24 07:47:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":979975,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4483213/v1_covered_8b4c33e9-ada2-4a72-a434-34c2e45e315f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning-based Visual Risk Warning System for Autonomous Driving","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":"Risk warning, Deep learning, Machine vision, YOLOv8","lastPublishedDoi":"10.21203/rs.3.rs-4483213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4483213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn autonomous driving, the identification and tracking of multiple vehicles on the road are critical tasks. This paper aims to develop a risk warning system using deep learning algorithms to address the heterogeneous, high-dynamic, and complex driving environments. To enhance the generalization capability and detection accuracy of small objects in road perception, we propose a novel VBFNet-YOLOv8 algorithm for real-time vehicle identification, tracking, distance measurement, and speed estimation. Specifically, we replace the Backbone of the original YOLOv8 network with the VanillaNet structure and upgrade the traditional PANet in the neck part to Bi-FPN. By integrating the optimized YOLOv8n algorithm with Deepsort and TTC algorithms, we achieve a comprehensive road risk assessment. The algorithm continuously tracks the targets, and the TTC algorithm intuitively assesses the risk. Finally, the system provides layered warnings by changing the color of the bounding boxes, offering drivers an integrated and real-time risk alert. 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