DMMNet: A Pedestrian Trajectory Prediction Method based on Decomposed Multimodal Modelling of Human Dynamics

DMMNet: A Pedestrian Trajectory Prediction Method based on Decomposed Multimodal Modelling of Human Dynamics | 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 DMMNet: A Pedestrian Trajectory Prediction Method based on Decomposed Multimodal Modelling of Human Dynamics Yanfei Gao, Xiongwei Miao, Zhang Guoye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4284368/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 Pedestrian trajectory prediction has wide applications in various engineering disciplines and in autonomous driving. It can effectively enhance traffic safety, improve traffic flow, optimize urban planning, and enhance the safety of autonomous driving. However, current methods can only perform unimodal predictions and cannot consider the diversity and uncertainty of pedestrian behavior. While some generative models can generate diverse prediction results, they cannot guarantee coverage of key modes and have limited control over the attributes of predicted trajectories. To address these issues, we propose a pedestrian trajectory prediction method based on Decomposed Multimodal Modeling of human dynamics, DMMNet, considering both the uncertainty of decision variables and the stochastic nature of random decision. Firstly, we adopt a decomposed modeling approach to effectively model the target uncertainty and the randomness of targets and paths in pedestrian trajectory prediction. This allows us to better capture the diversity and uncertainty of pedestrian behavior. Secondly, our method can generate explicit probability maps, providing better spatial constraints and control capabilities to improve the accuracy and interpretability of prediction results. This, in turn, offers better guidance and adaptability for other intelligent systems. Finally, our method extends the prediction range, capable of predicting pedestrian trajectories over a longer time period in the future. The proposed pedestrian trajectory prediction method in this paper has clear advantages in considering multimodality, providing spatial constraints, and expanding the application scope. It can offer more accurate, interpretable, and controllable pedestrian trajectory prediction capabilities for intelligent transportation systems and other related research and applications. The proposed method achieved an improvement of 47.7% in the ADE metric and 62.6% in the FDE metric on the ETH/UCY dataset. In the SDD dataset, there was an improvement of 18.4% in the ADE metric and 35.2% in the FDE metric. Trajectory forecasting Scene feature encoding Interaction feature encoding Destination encoding Walkable area 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-4284368","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292601147,"identity":"78f27897-3890-4fbc-ba0d-40efae2db078","order_by":0,"name":"Yanfei Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3RMYvCMBTA8ZTAm4pZX1B6X+GBUPDbpCCd9BBcKghXUdpB7nT1Wzi6SqBT5Fbd6kdwu0HQnqti6uaQ3/z+JC9hzHHeEIipLuUZvgT3JqVKxvakgUVMnbShZM41laawJwHrEY7SQNEvxPI44zUuxgzRYRN+kvbDJEqBiXyunic8GxxXJh7KKtlHmxZDs1tbTtHrNkLh/dwSA4ywb0sUNc9w8dIqGUQZr5N0CWUG0VJDzOol/4+MBtpyyjUqU/jWXT4W1VdiAoEQ28npLxkHIv9+ntzxXxt3HMdxHroCveBKLSmtUKsAAAAASUVORK5CYII=","orcid":"","institution":"Shanxi Finance \u0026 Taxation College","correspondingAuthor":true,"prefix":"","firstName":"Yanfei","middleName":"","lastName":"Gao","suffix":""},{"id":292601148,"identity":"122d5d90-ba78-4672-9ef5-e3225a976d88","order_by":1,"name":"Xiongwei Miao","email":"","orcid":"","institution":"Shanxi Intelligent Big Data Industry Technology Innovation Research Institute TaiYuan","correspondingAuthor":false,"prefix":"","firstName":"Xiongwei","middleName":"","lastName":"Miao","suffix":""},{"id":292601150,"identity":"4e7ee16c-e40a-440c-8fa3-30850a302504","order_by":2,"name":"Zhang Guoye","email":"","orcid":"","institution":"Shanxi Intelligent Big Data Industry Technology Innovation Research Institute TaiYuan","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Guoye","suffix":""}],"badges":[],"createdAt":"2024-04-18 01:14:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4284368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4284368/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55954699,"identity":"75b96739-e63c-4408-a6f7-ab9ef8124d2c","added_by":"auto","created_at":"2024-05-06 19:40:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":706499,"visible":true,"origin":"","legend":"","description":"","filename":"DMMNetAPedestrianTrajectoryPredictionMethodbasedonDecomposedMultimodalModellingofHumanDynamics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4284368/v1_covered_9034e049-8003-478a-9589-f48ff7937845.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DMMNet: A Pedestrian Trajectory Prediction Method based on Decomposed Multimodal Modelling of Human Dynamics","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":"Trajectory forecasting, Scene feature encoding, Interaction feature encoding, Destination encoding, Walkable area","lastPublishedDoi":"10.21203/rs.3.rs-4284368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4284368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePedestrian trajectory prediction has wide applications in various engineering disciplines and in autonomous driving. 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Secondly, our method can generate explicit probability maps, providing better spatial constraints and control capabilities to improve the accuracy and interpretability of prediction results. This, in turn, offers better guidance and adaptability for other intelligent systems. Finally, our method extends the prediction range, capable of predicting pedestrian trajectories over a longer time period in the future. The proposed pedestrian trajectory prediction method in this paper has clear advantages in considering multimodality, providing spatial constraints, and expanding the application scope. It can offer more accurate, interpretable, and controllable pedestrian trajectory prediction capabilities for intelligent transportation systems and other related research and applications. The proposed method achieved an improvement of 47.7% in the ADE metric and 62.6% in the FDE metric on the ETH/UCY dataset. 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