Spatiotemporal Variational Autoencoder forIntersection Pedestrian Trajectory Prediction

Spatiotemporal Variational Autoencoder forIntersection Pedestrian Trajectory Prediction | 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 Spatiotemporal Variational Autoencoder forIntersection Pedestrian Trajectory Prediction Qing Yu, Zhenwei Xu, Yaoyong Zhou, Jiaqi Zeng, Wushouer Silamu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4636478/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 This paper proposes an innovative Spatiotemporal Variational Autoencoder (ST-VAE) model, aimed at effectively capturing the spatiotemporal features of pedestrian behavior to enhance the accuracy of pedestrian trajectory prediction at intersections. To address the complex relationships in pedestrian social interactions, the paper designs a Social Graph Attention Network (SGAT) that dynamically identifies the relative importance of traffic participants, optimizing interaction analysis. Additionally, the paper introduces a Complex Gated Recurrent Unit (CGRU) to accurately capture the dynamic changes in time series data, thereby optimizing future trajectory predictions.To eliminate bias and improve prediction accuracy, the paper designs a Diversity-Enhanced Final Position Clustering (DEFPC) method as a post-processing technique. Evaluation results on four public datasets show that the ST-VAE model reduces the Average Displacement Error (ADE) and Final Displacement Error (FDE) by 27.2% and 18.2%, respectively, compared to the current best benchmark model SocialCVAE, demonstrating significant performance advantages. Further ablation studies reveal the specific contributions of each component to the model's performance, confirming the effectiveness and robustness of ST-VAE in handling diverse environments and complex interaction scenarios.Overall, the ST-VAE model not only provides new perspectives for pedestrian trajectory prediction technology but also offers solid technical support for the development of autonomous driving planning systems, showcasing the immense potential of deep learning in intelligent transportation system applications. Through innovative model design and comprehensive experimental validation, this paper provides new methodologies for efficient pedestrian trajectory prediction, advancing the development of the intelligent transportation field. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Software Physical sciences/Mathematics and computing/Information technology 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4636478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":333200671,"identity":"fe72fe50-3e26-4fe9-a927-c4273f762105","order_by":0,"name":"Qing Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIie3RMYvCMBTA8RcCdXmta0oPex8hxemgcF8lUuh0QydXC4XeIs76LRS/QI8MtxRdO9al80HhOA4HUxXH2FEw/+2F/PKGAJhMD1oBwNCntAB2HfuQ8CX4tER/oopD2CO/vaCNV9FGJv8SSYa/7C2HkVMJ0iZaEidytZA4oPaWuzmM3UpQb6klH1za826LvakVmawrYVHsQ0BiXSgy60fwL+4I6bYIfo+4ZZNIOw0xyKwxZzsWrMpD5umI8x1tWzyyd38oG49NQ1+dfLU68loAB5JfBsrOn0lSDQDwU0XgeBnIj/auyWQyPWsn5sVNOpawK/0AAAAASUVORK5CYII=","orcid":"","institution":"Xinjiang University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"Yu","suffix":""},{"id":333200672,"identity":"9bc7bd8f-e4cc-4e77-a624-0931acbda15b","order_by":1,"name":"Zhenwei Xu","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Zhenwei","middleName":"","lastName":"Xu","suffix":""},{"id":333200673,"identity":"340fbac7-23e0-4748-810c-c96c630ee5fe","order_by":2,"name":"Yaoyong Zhou","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Yaoyong","middleName":"","lastName":"Zhou","suffix":""},{"id":333200674,"identity":"86790544-83dd-4a46-855d-a3e9e09365df","order_by":3,"name":"Jiaqi Zeng","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Zeng","suffix":""},{"id":333200675,"identity":"17c6d350-45c8-4189-a69d-d951803bcb6a","order_by":4,"name":"Wushouer Silamu","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Wushouer","middleName":"","lastName":"Silamu","suffix":""}],"badges":[],"createdAt":"2024-06-25 12:42:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4636478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4636478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68232166,"identity":"9bc13c76-f68c-4f30-bde8-4b620959dfd8","added_by":"auto","created_at":"2024-11-05 06:17:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1290110,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports21.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4636478/v1_covered_bc99cd28-4b57-4a1c-9d0f-a123ed286768.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Variational Autoencoder forIntersection Pedestrian Trajectory Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4636478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4636478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper proposes an innovative Spatiotemporal Variational Autoencoder (ST-VAE) model, aimed at effectively capturing the spatiotemporal features of pedestrian behavior to enhance the accuracy of pedestrian trajectory prediction at intersections. To address the complex relationships in pedestrian social interactions, the paper designs a Social Graph Attention Network (SGAT) that dynamically identifies the relative importance of traffic participants, optimizing interaction analysis. Additionally, the paper introduces a Complex Gated Recurrent Unit (CGRU) to accurately capture the dynamic changes in time series data, thereby optimizing future trajectory predictions.To eliminate bias and improve prediction accuracy, the paper designs a Diversity-Enhanced Final Position Clustering (DEFPC) method as a post-processing technique. Evaluation results on four public datasets show that the ST-VAE model reduces the Average Displacement Error (ADE) and Final Displacement Error (FDE) by 27.2% and 18.2%, respectively, compared to the current best benchmark model SocialCVAE, demonstrating significant performance advantages. Further ablation studies reveal the specific contributions of each component to the model's performance, confirming the effectiveness and robustness of ST-VAE in handling diverse environments and complex interaction scenarios.Overall, the ST-VAE model not only provides new perspectives for pedestrian trajectory prediction technology but also offers solid technical support for the development of autonomous driving planning systems, showcasing the immense potential of deep learning in intelligent transportation system applications. 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