SafeDrive-V2V: A Real-Time Driver Distraction Detection Framework Using CIRNet and V2V Safety Communication

SafeDrive-V2V: A Real-Time Driver Distraction Detection Framework Using CIRNet and V2V Safety Communication | 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 SafeDrive-V2V: A Real-Time Driver Distraction Detection Framework Using CIRNet and V2V Safety Communication Sivaranjani Ramachandran, Priya Varadharajan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8109413/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Driver distraction constitutes a predominant factor which is the main reason for road accidents globally, representing a considerable increase in the percentage of traffic-related injuries and death rates. The timely identification of distracted drivers is imperative for the prevention of collisions and the assurance of road safety, particularly in increasing complexity of contemporary driving environments. This research work introduces an innovative framework named SafeDrive-V2V, which aims to detect distracted drivers in real-time and send vehicle-to-vehicle (V2V) alerts via Dedicated Short-Range Communication (DSRC). A hybrid deep learning architecture, referred to as CIRNet (Capsule-Infused ResNet), has been developed by augmenting the ResNet50 model with capsule layers, which serve to maintain spatial hierarchies and enhance the recognition of distracted behavioral indicators. The model undergoes training by utilizing the State Farm distracted driver dataset and is validated for its high accuracy in classifying diverse distracted states. For real-time application, a Raspberry Pi 4 equipped with a Pi camera is installed within the vehicle to perpetually capture images of the driver. These inputs are subsequently processed by the pre-trained CIRNet model for the detection of distraction in real time. Upon the identification of a distracted state, the system generates a Basic Safety Message (BSM), which is transmitted to proximate vehicles utilizing DSRC. This anticipatory communication is designed to caution other drivers regarding potential hazards, thereby mitigating the likelihood of collision. The proposed system presents a holistic solution that integrates deep learning-based monitoring with V2V communication to augment road safety. The model's effectiveness in real-time scenarios is confirmed by experimental results, confirming its feasibility for use in intelligent transportation systems. Driver Distraction Detection Real-Time Monitoring CIRNet Vehicle-to-Vehicle (V2V) Communication Dedicated Short Range Communication (DSRC) Basic Safety Message (BSM) Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Feb, 2026 Editor invited by journal 28 Nov, 2025 First submitted to journal 13 Nov, 2025 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-8109413","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584393295,"identity":"add56046-9617-4c43-b4f0-a230ecc852af","order_by":0,"name":"Sivaranjani Ramachandran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYDACZjjF2HDwg4GNHIh34AGRWhoPSxSkGYO1JBBr4QGeD4cTG0BMfFrk23kMP1fU3GHnn93ccEDCgDl9ftjhh0Bb7OR0G7BrMTjMYyx55tgzZok7BxsOFBiw5W68nWYA1JJsbHYAhxZmtgTJBrbDzAw3EkG28ORunJ0A0nIgcRsOLfLNbMk/G/4dZpYHaeExkEg3nJ3+Aa8WhsPMxyQb2w4zG0C0GCTIS+fgt8UAqMWyse8wsyFQy2EJgwTDDdI5BQcSDHD7Rb7/YPPNhm+Hk+VupD/++OHPf3n52embP3yosJPDpQUGkhH2glUa4FcOAnYIexsIqx4Fo2AUjIKRBQBwLmbjiZ3GmgAAAABJRU5ErkJggg==","orcid":"","institution":"KPRIET: KPR Institute of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Sivaranjani","middleName":"","lastName":"Ramachandran","suffix":""},{"id":584393296,"identity":"18a47ffc-e463-4f44-bb42-9dbb4837fefb","order_by":1,"name":"Priya Varadharajan","email":"","orcid":"","institution":"KPRIET: KPR Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Priya","middleName":"","lastName":"Varadharajan","suffix":""}],"badges":[],"createdAt":"2025-11-14 00:31:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8109413/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8109413/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101833367,"identity":"00aa480d-62f9-403b-a6d5-2729934530bf","added_by":"auto","created_at":"2026-02-04 06:56:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":735895,"visible":true,"origin":"","legend":"","description":"","filename":"renamededd4f.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8109413/v1_covered_2253871f-143b-46b5-b999-8d001e6addc7.pdf"}],"financialInterests":"","formattedTitle":"SafeDrive-V2V: A Real-Time Driver Distraction Detection Framework Using CIRNet and V2V Safety Communication","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Driver Distraction Detection, Real-Time Monitoring, CIRNet, Vehicle-to-Vehicle (V2V) Communication, Dedicated Short Range Communication (DSRC), Basic Safety Message (BSM)","lastPublishedDoi":"10.21203/rs.3.rs-8109413/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8109413/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDriver distraction constitutes a predominant factor which is the main reason for road accidents globally, representing a considerable increase in the percentage of traffic-related injuries and death rates. 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