DTANet: Dynamic Topology-Aware Network for Lane Detection in Complex Scenarios

DTANet: Dynamic Topology-Aware Network for Lane Detection in Complex Scenarios | 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 DTANet: Dynamic Topology-Aware Network for Lane Detection in Complex Scenarios Hongwen Yu, Wangjie Cong, Zini Wang, Qian Dong, Junjie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8371489/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Robust and accurate lane detection remains a critical challenge in autonomous driving, particularly under complex road scenarios such as occlusions, intersections , and curved lanes. Existing methods often struggle to preserve the topological structure of lanes due to limited geometric modeling capacity. To address this, we propose the Dynamic Topology-Aware Network (DTANet), which explicitly models and dynamically adapts the topology of lane during learning: it maintains the continuity of the lane under occlusions via adaptive contextual aggregation and accurately models curved lanes using differential geometric constraints. Specifically, DTANet has three core components: a Dynamic Topology Encoder (DTE) transforms spatial features into a graph-based representation , capturing local-global topological dependencies via multi-scale graph convolutions and dynamic attention; a Topology Distillation Enhancer (TDE) combines an adaptive gated mechanism with a geometric constraint—this constraint aligns curvature and tangent direction fields across feature levels to avoid semantic enhancement eroding lane geometric integrity, and the gated mechanism adjusts distillation pathways by scene complexity to reinforce continuity in degraded regions; a Topology Consistency Loss (TCL) supervises predicted curves via differential geometric constraints and penalizes curvature and orientation discrepancies against ground truth to provide global topological supervision. Experimental results on challenging lane detection benchmarks indicate that our method demonstrates promising performance compared against state-of-the-art models. Lane detection topology-aware learning curvature constraint structural consistency Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor assigned by journal 16 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 15 Dec, 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. 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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-8371489","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623937985,"identity":"82971e41-656a-4241-8e7d-de1961afe4c0","order_by":0,"name":"Hongwen Yu","email":"","orcid":"","institution":"Shanghai University","correspondingAuthor":false,"prefix":"","firstName":"Hongwen","middleName":"","lastName":"Yu","suffix":""},{"id":623937986,"identity":"5e20f1e1-d966-4db6-8d36-9261a7a3c61b","order_by":1,"name":"Wangjie Cong","email":"","orcid":"","institution":"Shanghai University","correspondingAuthor":false,"prefix":"","firstName":"Wangjie","middleName":"","lastName":"Cong","suffix":""},{"id":623937987,"identity":"e26236eb-b33d-418a-b36d-11ab34188fb7","order_by":2,"name":"Zini Wang","email":"","orcid":"","institution":"Jingdezhen University","correspondingAuthor":false,"prefix":"","firstName":"Zini","middleName":"","lastName":"Wang","suffix":""},{"id":623937988,"identity":"c94534e6-2a9d-40bb-82bb-767739b1f3c7","order_by":3,"name":"Qian Dong","email":"","orcid":"","institution":"Xi’an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Dong","suffix":""},{"id":623937989,"identity":"f5314465-71d1-4dab-aa90-e81599712935","order_by":4,"name":"Junjie Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3RsUrDQBzH8V8I3C0xxe1Kh77CPwRSxfouJ4LTWTpJh2IDwk19gA76Dq5uESFTtKtjxNEIGRVBvIQ45ugoeN8h/AP/D3dwgMv1B+MpQBJYUfNXgyFshsBCvI74DfE2YILtQppa4pvNHcjmPJuXC7AJf3x4O9bhJRPSK981xpNeMpMkCwSH69nZkSrMxYT0oxuN6C7tI4roRENQppJYLVrCRnsakjIb+QbRtkriA2oJ/7KR/ZakZuFZxa/oTvFtZBRURDIXhlSJty7YUAcvV8PrJxHd9pCQqyT6WE5XtFVx/anzwYCf3tfVxXTcd4qJmScR7WC+Obq3Er37Jr/8HWpgadt0uVyuf9oPxXhPvVhEzQ4AAAAASUVORK5CYII=","orcid":"","institution":"Xi’an Jiaotong-Liverpool University","correspondingAuthor":true,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-12-16 03:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8371489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8371489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107482261,"identity":"e3209009-e236-4f0a-aec3-38e0e2ab9dc7","added_by":"auto","created_at":"2026-04-22 02:22:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1553154,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerDTANet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8371489/v1_covered_a4bd74d1-f782-4e69-898e-9a945c4984b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DTANet: Dynamic Topology-Aware Network for Lane Detection in Complex Scenarios","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":"machine-vision-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mvap","sideBox":"Learn more about [Machine Vision and Applications](https://www.springer.com/journal/138)","snPcode":"138","submissionUrl":"https://submission.springernature.com/new-submission/138/3","title":"Machine Vision and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lane detection, topology-aware learning, curvature constraint, structural consistency","lastPublishedDoi":"10.21203/rs.3.rs-8371489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8371489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Robust and accurate lane detection remains a critical challenge in autonomous driving, particularly under complex road scenarios such as occlusions, intersections , and curved lanes. 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