A Hybrid Machine Learning Approach for Scalable and Uncertainty-Aware RSU Tracking in VANET

A Hybrid Machine Learning Approach for Scalable and Uncertainty-Aware RSU Tracking in VANET | 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 Hybrid Machine Learning Approach for Scalable and Uncertainty-Aware RSU Tracking in VANET KRISHNA KOMARAM, NAGARJUNA KARYEMSETTY This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7633199/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Journal on Wireless Communications and Networking → Version 1 posted You are reading this latest preprint version Abstract This study addresses the challenge of real-time Roadside Unit (RSU) tracking in Vehicle-to-Vehicle (V2V) networks, where existing methods struggle with accuracy, scalability (100–25,000 vehicles), and noisy data. Traditional approaches lack adaptive feature learning and uncertainty handling, leading to misclassification in dynamic traffic conditions. To overcome these limitations, we propose a hybrid machine learning framework combining XGBoost (for high-accuracy classification) and a Gradient Discriminant Filter (GDF) (for probabilistic refinement), enhanced by Bayesian Deep Neural Network (BDN)-based feature transformation. The system processes real-time simulated vehicle tracking data, optimizing feature representations for improved interpretability and performance. XGBoost handles nonlinear decision boundaries, while GDF refines predictions under uncertainty. BDN modeling further enhances robustness by quantifying prediction confidence. Experimental results demonstrate over 95% classification accuracy with efficient scalability across varying traffic densities. The proposed method outperforms conventional techniques in both precision and computational efficiency, making it suitable for large-scale intelligent transportation systems (ITS). This work advances real-time V2V analytics by integrating ensemble learning, probabilistic filtering, and deep feature transformation for reliable RSU tracking in dynamic environments. RSU Tracking V2V Communication XGBoost Gradient Discriminant Filter (GDF) Feature Transformation Bayesian Dense Neural Network (BDN) Real-Time Tracking Machine Learning Full Text Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Journal on Wireless Communications and Networking → 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-7633199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551159075,"identity":"3dc850d5-f7e3-4c3d-92c2-63322e4a80cf","order_by":0,"name":"KRISHNA 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VANET","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"RSU Tracking, V2V Communication, XGBoost, Gradient Discriminant Filter (GDF), Feature Transformation, Bayesian Dense Neural Network (BDN), Real-Time Tracking, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-7633199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7633199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study addresses the challenge of real-time Roadside Unit (RSU) tracking in Vehicle-to-Vehicle (V2V) networks, where existing methods struggle with accuracy, scalability (100–25,000 vehicles), and noisy data. Traditional approaches lack adaptive feature learning and uncertainty handling, leading to misclassification in dynamic traffic conditions. To overcome these limitations, we propose a hybrid machine learning framework combining XGBoost (for high-accuracy classification) and a Gradient Discriminant Filter (GDF) (for probabilistic refinement), enhanced by Bayesian Deep Neural Network (BDN)-based feature transformation. The system processes real-time simulated vehicle tracking data, optimizing feature representations for improved interpretability and performance. XGBoost handles nonlinear decision boundaries, while GDF refines predictions under uncertainty. BDN modeling further enhances robustness by quantifying prediction confidence. Experimental results demonstrate over 95% classification accuracy with efficient scalability across varying traffic densities. The proposed method outperforms conventional techniques in both precision and computational efficiency, making it suitable for large-scale intelligent transportation systems (ITS). This work advances real-time V2V analytics by integrating ensemble learning, probabilistic filtering, and deep feature transformation for reliable RSU tracking in dynamic environments.\u003c/p\u003e","manuscriptTitle":"A Hybrid Machine Learning Approach for Scalable and Uncertainty-Aware RSU Tracking in VANET","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 15:41:24","doi":"10.21203/rs.3.rs-7633199/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":"6e532b25-4e80-48ca-9976-b9fb49c13f69","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:04:07+00:00","versionOfRecord":{"articleIdentity":"rs-7633199","link":"https://doi.org/10.1186/s13638-026-02616-7","journal":{"identity":"journal-on-wireless-communications-and-networking","isVorOnly":true,"title":"Journal on Wireless Communications and Networking"},"publishedOn":"2026-04-22 15:59:14","publishedOnDateReadable":"April 22nd, 2026"},"versionCreatedAt":"2025-12-01 15:41:24","video":"","vorDoi":"10.1186/s13638-026-02616-7","vorDoiUrl":"https://doi.org/10.1186/s13638-026-02616-7","workflowStages":[]},"version":"v1","identity":"rs-7633199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7633199","identity":"rs-7633199","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}