Identification of Dynamic Driving Styles based on Behavioral Primitives: A Research from Data to Insights

Identification of Dynamic Driving Styles based on Behavioral Primitives: A Research from Data to Insights | 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 Article Identification of Dynamic Driving Styles based on Behavioral Primitives: A Research from Data to Insights Xuelian Zheng, Wenyu Kang, Yuanyuan Ren, Xiansheng Li, Jianfeng Xi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6694518/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Driving style reflects a driver's vehicle manipulation and driving habits, it is essential for understanding and analyzing a driver’s dynamic behavior and decision-making process. To better understand the intrinsic characteristics of how drivers make decisions and control their vehicles based on external conditions, this paper proposes an unsupervised framework for dynamic driving style recognition based on driving behavior primitives. The framework consists of three key stages: primitive extraction, model development, and dynamic driving style recognition. Unsupervised techniques are employed to extract meaningful driving behavior primitives from long-term driving data. The primitive serves as the fundamental unit for dynamic driving style analysis, and a driving style assessment model is built using a linear weighting approach. This model quantifies the risks of primitives and the transition risks between adjacent primitives. The thresholds for classifying cautious, average, and aggressive driving styles are determined using an improved particle swarm optimization algorithm. The proposed dynamic driving style recognition framework comprehensively considers the characteristics of the current primitive and its surrounding context. By preserving the temporal features of driving behavior, the framework provides fine-grained recognition of dynamic driving styles. Additionally, a deeper understanding of a driver’s dynamic driving style and their long-term driving behavior can be gained based on this framework, which will benefit the development of AVs and ADASs. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Scientific data dynamic driving style behavioral primitive primitive risk primitives transition risk the dynamic driving style assessment model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviews received at journal 19 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviews received at journal 08 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 05 Jun, 2025 Editor invited by journal 02 Jun, 2025 Submission checks completed at journal 31 May, 2025 First submitted to journal 18 May, 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. 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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-6694518","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":484321634,"identity":"6f9689a9-2e2a-4f7f-904a-b448df733a66","order_by":0,"name":"Xuelian Zheng","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Xuelian","middleName":"","lastName":"Zheng","suffix":""},{"id":484321635,"identity":"5d99c68c-5e37-46e4-9bae-f6a97d00a77d","order_by":1,"name":"Wenyu Kang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Wenyu","middleName":"","lastName":"Kang","suffix":""},{"id":484321636,"identity":"6ace25a8-b21e-49d9-bc14-59d29e33876a","order_by":2,"name":"Yuanyuan Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYNCCCjYZECVBgpYzbDwkamFsYyBBi3l77zFp3nl8PAYHmA/e5mGwyyOoRebMuTRp3m1sQC1sydY8DMnFBLVISOSYQbXwmEnzMBxIbCCoRf4NUMsckBb+b0RqkQAaztsAtoWNSC08OcaWc46x8UgeZgMyDJKJ0MJ+xvDGm5pjcnzHmx/eeFNhR1gLELBI8TAcY2BgBrENiFAPBMwffzDUEKd0FIyCUTAKRiYAAGoCL9n4je2IAAAAAElFTkSuQmCC","orcid":"","institution":"Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Ren","suffix":""},{"id":484321637,"identity":"43d8d00a-58c8-43ee-8648-646be36c1ce3","order_by":3,"name":"Xiansheng Li","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Xiansheng","middleName":"","lastName":"Li","suffix":""},{"id":484321638,"identity":"8e4ecf5a-59ab-4ddc-8524-352fcb1b9421","order_by":4,"name":"Jianfeng Xi","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jianfeng","middleName":"","lastName":"Xi","suffix":""}],"badges":[],"createdAt":"2025-05-19 03:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6694518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6694518/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-38787-y","type":"published","date":"2026-03-26T16:08:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105754892,"identity":"1257f9a3-9d6c-4bf6-bc0e-ee74deef58b1","added_by":"auto","created_at":"2026-03-30 16:22:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1073056,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6694518/v1_covered_053e5fdf-7554-4388-a08f-a150d1209fd4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Dynamic Driving Styles based on Behavioral Primitives: A Research from Data to Insights","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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"dynamic driving style, behavioral primitive, primitive risk, primitives transition risk, the dynamic driving style assessment model","lastPublishedDoi":"10.21203/rs.3.rs-6694518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6694518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDriving style reflects a driver's vehicle manipulation and driving habits, it is essential for understanding and analyzing a driver\u0026rsquo;s dynamic behavior and decision-making process. To better understand the intrinsic characteristics of how drivers make decisions and control their vehicles based on external conditions, this paper proposes an unsupervised framework for dynamic driving style recognition based on driving behavior primitives. The framework consists of three key stages: primitive extraction, model development, and dynamic driving style recognition. Unsupervised techniques are employed to extract meaningful driving behavior primitives from long-term driving data. The primitive serves as the fundamental unit for dynamic driving style analysis, and a driving style assessment model is built using a linear weighting approach. This model quantifies the risks of primitives and the transition risks between adjacent primitives. The thresholds for classifying cautious, average, and aggressive driving styles are determined using an improved particle swarm optimization algorithm. 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