Dynamic Risk-Aware Lane Change Decision-Making for Autonomous Vehicles Using Deep Contextual Learning

Dynamic Risk-Aware Lane Change Decision-Making for Autonomous Vehicles Using Deep Contextual Learning | 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 Dynamic Risk-Aware Lane Change Decision-Making for Autonomous Vehicles Using Deep Contextual Learning Lakshmi Narayana, Dr.TMN Vamsi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7672099/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 real traffic, change a lane safely mostly relies on the system’s ability to judge the distance to the car behind and in front of it. Applying rigid rules with specific limits frequently ends up being too restrictive and doesn’t help when making decisions about changing a lane. In this research, I propose a new system that learns from the context and improves it with reinforcement learning that makes it a more accurate and reliable system. To understand the lane change risks in the traffic at the moment I apply ResNet50 with transfer learning and enhance it with LSTM layers. To detect and track cars and also know what they might do I use Mask R-CNN with CNN and LSTM so that all these three things can be done by the model.Since the traffic conditions are always different I also apply an analysis of the weather, speed, acceleration, steering angle, and the road surface conditions as additional inputs to the system.To make the decisions safer I added a Double Deep Q-Network, which was found to be a steadier and faster to train than older reinforcement learning methods in heavy traffic conditions. From the simulation results, we can see that the check of the risks is clearer and more accurate, the decisions are better, and the change of a lane is smoother. So the system is more safe and reliable, and we move one step closer to the smarter transport systems. Autonomous Vehicles Lane Change Decision-Making Risk Assessment Inter-Vehicle Distance Intelligent Transportation Systems 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. We do this by developing innovative software and high quality services for the global research community. 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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-7672099","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533755185,"identity":"fa9ba67e-f3da-4c7e-827e-f2a23141dfd1","order_by":0,"name":"Lakshmi 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Transportation Systems","lastPublishedDoi":"10.21203/rs.3.rs-7672099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7672099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn real traffic, change a lane safely mostly relies on the system\u0026rsquo;s ability to judge the distance to the car behind and in front of it. Applying rigid rules with specific limits frequently ends up being too restrictive and doesn\u0026rsquo;t help when making decisions about changing a lane. In this research, I propose a new system that learns from the context and improves it with reinforcement learning that makes it a more accurate and reliable system. To understand the lane change risks in the traffic at the moment I apply ResNet50 with transfer learning and enhance it with LSTM layers. To detect and track cars and also know what they might do I use Mask R-CNN with CNN and LSTM so that all these three things can be done by the model.Since the traffic conditions are always different I also apply an analysis of the weather, speed, acceleration, steering angle, and the road surface conditions as additional inputs to the system.To make the decisions safer I added a Double Deep Q-Network, which was found to be a steadier and faster to train than older reinforcement learning methods in heavy traffic conditions. From the simulation results, we can see that the check of the risks is clearer and more accurate, the decisions are better, and the change of a lane is smoother. So the system is more safe and reliable, and we move one step closer to the smarter transport systems.\u003c/p\u003e","manuscriptTitle":"Dynamic Risk-Aware Lane Change Decision-Making for Autonomous Vehicles Using Deep Contextual Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-25 08:06:21","doi":"10.21203/rs.3.rs-7672099/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":"d20077b6-7369-43c4-8e70-23e9611a9f1e","owner":[],"postedDate":"October 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T21:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-25 08:06:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7672099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7672099","identity":"rs-7672099","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}