Robust Visual-based Method and New Datasets for Ego-lane Index Estimation in Urban Environment

Robust Visual-based Method and New Datasets for Ego-lane Index Estimation in Urban Environment | 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 Robust Visual-based Method and New Datasets for Ego-lane Index Estimation in Urban Environment Dianzheng Wang, Dongyi Liang, Shaomiao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4193043/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Aug, 2024 Read the published version in Machine Vision and Applications → Version 1 posted 10 You are reading this latest preprint version Abstract Correct and robust ego-lane index estimation is crucial for autonomous in the absence of high-definition maps, especially in urban environments. Previous ego-lane index estimation approaches rely on feature extraction, which limited the robustness. To overcome these shortages, this study proposes a robust ego-lane index estimation framework upon only the original visual image. After optimization of processing route, the raw image was randomly cropped in height direction and then input into double supervised LaneLoc network to obtain the index estimations and confidences. A post process was also proposed to achieve the global ego-lane index from the estimated left and right indexes with total lane number. To evaluate our proposed method, we manually annotated the ego-lane index of public datasets which can work as an ego-lane index estimation baseline for the first time. The proposed algorithm achieved 96.48%/95.40% (precision/recall) on CULane dataset and 99.45%/99.49% (precision/recall) on TuSimple dataset, demonstrating the effectiveness and efficiency of lane localization in diverse driving environments. The code and datasets annotation results will be exposed publicly on https://github.com/haomo-ai/LaneLoc . Ego-lane index estimation Visual image Dataset Double supervision Urban environments Autonomous driving Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Aug, 2024 Read the published version in Machine Vision and Applications → Version 1 posted Editorial decision: Revision requested 25 Jun, 2024 Reviews received at journal 23 Jun, 2024 Reviewers agreed at journal 09 Jun, 2024 Reviews received at journal 19 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers invited by journal 22 Apr, 2024 Editor assigned by journal 31 Mar, 2024 Submission checks completed at journal 31 Mar, 2024 First submitted to journal 30 Mar, 2024 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-4193043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285982005,"identity":"871f6f59-a050-4a0e-9b81-7fb97f2689da","order_by":0,"name":"Dianzheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYLACHgYbOX7mAyCmhAyxWtKMJdsSwFp4iNVyOHHDsQQomxAwOH728Is3fw4nbj7GfEzqRo0FDwP74aMb8Go5k5dmOYcn3XjbMbZk45xjQIfxpKXdwKfF7ECOmTGPhLXstvs9ho9z2IBaJHjM8Gs5/waoxYCZcXMbj8HhnH/EaLmRY/yYJ8FZcQMbj+Hj3DYitNjfeGPGOOdAmrEEyC+5fRI8bIT8ItmfY/zhzR9gVLYxH5PO+VYnx89++BheLUDAJoHKJaAcBJg/EKFoFIyCUTAKRjIAAFBFRa+4FOoYAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Dianzheng","middleName":"","lastName":"Wang","suffix":""},{"id":285982006,"identity":"d5dc56e7-daaa-40d9-ace2-0a74cde2455b","order_by":1,"name":"Dongyi Liang","email":"","orcid":"","institution":"HAOMO.AI Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Dongyi","middleName":"","lastName":"Liang","suffix":""},{"id":285982007,"identity":"7cf151c0-60be-48be-bd79-a212822bb1a7","order_by":2,"name":"Shaomiao Li","email":"","orcid":"","institution":"HAOMO.AI Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Shaomiao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-03-30 16:50:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4193043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4193043/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00138-024-01590-8","type":"published","date":"2024-08-14T15:58:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63071461,"identity":"6f8a9b67-c75d-49a9-acc1-f920cfd8d0ce","added_by":"auto","created_at":"2024-08-22 20:07:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1016235,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4193043/v1_covered_1bf08d68-2324-4232-8e6f-eaafa4bebcbe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Visual-based Method and New Datasets for Ego-lane Index Estimation in Urban Environment","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":"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":"Ego-lane index estimation, Visual image, Dataset, Double supervision, Urban environments, Autonomous driving","lastPublishedDoi":"10.21203/rs.3.rs-4193043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4193043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCorrect and robust ego-lane index estimation is crucial for autonomous in the absence of high-definition maps, especially in urban environments. Previous ego-lane index estimation approaches rely on feature extraction, which limited the robustness. To overcome these shortages, this study proposes a robust ego-lane index estimation framework upon only the original visual image. After optimization of processing route, the raw image was randomly cropped in height direction and then input into double supervised LaneLoc network to obtain the index estimations and confidences. A post process was also proposed to achieve the global ego-lane index from the estimated left and right indexes with total lane number. To evaluate our proposed method, we manually annotated the ego-lane index of public datasets which can work as an ego-lane index estimation baseline for the first time. The proposed algorithm achieved 96.48%/95.40% (precision/recall) on CULane dataset and 99.45%/99.49% (precision/recall) on TuSimple dataset, demonstrating the effectiveness and efficiency of lane localization in diverse driving environments. 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