FL-P4AV: A Federated Learning-Based Privacy-Preserving Personalized Path Planning Framework for Collaborative Autonomous Ground Vehicles

FL-P4AV: A Federated Learning-Based Privacy-Preserving Personalized Path Planning Framework for Collaborative Autonomous Ground Vehicles | 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 FL-P4AV: A Federated Learning-Based Privacy-Preserving Personalized Path Planning Framework for Collaborative Autonomous Ground Vehicles Saranya C, Janaki G This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7939959/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 The growing deployment of autonomous ground vehicles in smart cities and logistics demands secure, efficient, and context-aware navigation systems. This paper proposes FL-P4AV, a Federated Learning-Enabled Privacy-Preserving Personalized Path Planning framework designed for collaborative autonomous vehicles. Unlike traditional centralized or static path planners, FL-P4AV allows each vehicle to train a lightweight local model that predicts navigation costs based on semantic features such as obstacle density and goal proximity. The models undergo refinement via federated learning, which maintains privacy by not sharing raw data and employing differential privacy mechanisms. The semantic weights obtained are incorporated into an enhanced A* algorithm to facilitate personalized and efficient route computation. Experimental evaluations in dynamic grid environments indicate that FL-P4AV results in shorter paths, fewer inflection points, reduced turning angles, and quicker planning times relative to baseline methods. Despite the existence of privacy-preserving noise, the system maintains steady convergence and adjusts dynamically to real-time environmental changes. FL-P4AV offers a scalable and secure framework for the coordination of decentralized autonomous vehicles in path planning, indicating substantial potential for real-world applications in smart transportation systems. Federated Learning Privacy-Preserving Path Planning Autonomous Ground Vehicles Differential Privacy Semantic A* Algorithm 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-7939959","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534629383,"identity":"831d3774-d1af-434d-b33f-93f79b4f5f1a","order_by":0,"name":"Saranya C","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDADNvYGIGkAZhvAGPgBH88BsGIJ4rXISSSAaQkGQurl288+/FzYZifPJvn82acbBXV1DOzN2yQYCu7g1GJwJt1YemZbsmGbdI7x7ByDwxIMPMfKJBgMnuHWwpDGIM3bxpzAJp3DzJxjcECCQSLHDKjlMG6H9T9j/s3bVp/AJnn8MVBLnQSD/Bv8WhhupLEBbTmcwCbBYAzUwgy0hQe/FoMbz9isec4dN2zjyQFpOSzZxpNWbJGA12FpzLd5yqrl5dtBDvtTx8/PfnjjjQ9/8DgMBBjZkDhgdgJ+DUDwh6CKUTAKRsEoGMkAAODsRMRo2S4yAAAAAElFTkSuQmCC","orcid":"","institution":"SRM Institute of Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Saranya","middleName":"","lastName":"C","suffix":""},{"id":534629384,"identity":"549d1dc9-9082-47da-ba2a-a9019eefd22c","order_by":1,"name":"Janaki G","email":"","orcid":"","institution":"SRM Institute of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Janaki","middleName":"","lastName":"G","suffix":""}],"badges":[],"createdAt":"2025-10-24 19:44:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7939959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7939959/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94592233,"identity":"aed3117b-f9e9-4457-95a1-d341fceb1493","added_by":"auto","created_at":"2025-10-28 18:23:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1001818,"visible":true,"origin":"","legend":"","description":"","filename":"PaperFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7939959/v1_covered_cfd9202c-750f-4149-9220-1ed34b2a70da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFL-P4AV: A Federated Learning-Based Privacy-Preserving Personalized Path Planning Framework for Collaborative Autonomous Ground Vehicles\u003c/p\u003e","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":"Federated Learning, Privacy-Preserving Path Planning, Autonomous Ground Vehicles, Differential Privacy, Semantic A* Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-7939959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7939959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The growing deployment of autonomous ground vehicles in smart cities and logistics demands secure, efficient, and context-aware navigation systems. 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