Pedestrian trajectory prediction based on improved avoidance force algorithm

Pedestrian trajectory prediction based on improved avoidance force algorithm | 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 Pedestrian trajectory prediction based on improved avoidance force algorithm Jiazhe Miao, Yalong Kang, Tao Peng, Tongyu Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3922637/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 The complexity of interactions between pedestrians poses a challenge to pedestrian trajectory prediction, and existing trajectory prediction methods based on data-driven models lack interpretation for modeling interactions between pedestrians. To address this problem, an improved avoidance force algorithm is proposed to model the interaction of pedestrian forces explicitly. Multiple socially acceptable pedestrian trajectory information is generated by using the prior knowledge of observed trajectory and the avoidance force algorithm.The avoidance force trajectories are evaluated by an attention network to generate confidence scores; the avoidance force trajectories are selected based on the confidence scores; The final accurate trajectories are refined using Teacher-forcing. In comparison with recent approaches, our experimental results on the ETH and UCY datasets demonstrate a significant improvement in both Average Displacement Error (ADE) and Final Displacement Error (FDE) achieved by the proposed method. Social force Avoidance algorithm Self-attention Teacher-forcing 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-3922637","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272942050,"identity":"f5388e62-7822-4709-a8ea-cfe4821b5602","order_by":0,"name":"Jiazhe Miao","email":"","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Jiazhe","middleName":"","lastName":"Miao","suffix":""},{"id":272942051,"identity":"1cdee30b-1a7d-41b2-ab07-d88ac01fd5c8","order_by":1,"name":"Yalong Kang","email":"","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Yalong","middleName":"","lastName":"Kang","suffix":""},{"id":272942052,"identity":"37794750-9c53-4305-83bd-d9bafc7e1e24","order_by":2,"name":"Tao Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnUlEQVRIiWNgGAWjYFAC5gMQ+gDROtjYEiCqSdDCY0CiFnP5nm/SH9sY5PhuJDB+LiBGi2Ub7zaJg20MxpI3EpilZxCjxeAY77YbQC2JG24ksDHzEKeF5xlISz1JWthAWhIMiNZi2ZZm/uPMOQnDmWceNksTpcWc+fBjg4oyG3m+48kHPxPnMAglAcSMDcRogGsZBaNgFIyCUYAHAAC6ODCttMOW0QAAAABJRU5ErkJggg==","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Peng","suffix":""},{"id":272942053,"identity":"96fb2d2b-f2b1-42b0-8bd5-c648961f9d7b","order_by":3,"name":"Tongyu Liu","email":"","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Tongyu","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-02-03 04:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3922637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3922637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51541243,"identity":"2b09d8ae-b7cb-43d7-9574-7240808aa671","added_by":"auto","created_at":"2024-02-23 11:23:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":953362,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3922637/v1_covered_59d0e41b-4052-47a8-adf8-11b03df0cb91.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pedestrian trajectory prediction based on improved avoidance force algorithm","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":"Social force, Avoidance algorithm, Self-attention, Teacher-forcing","lastPublishedDoi":"10.21203/rs.3.rs-3922637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3922637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The complexity of interactions between pedestrians poses a challenge to pedestrian trajectory prediction, and existing trajectory prediction methods based on data-driven models lack interpretation for modeling interactions between pedestrians. 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