Heterogeneous graph social pooling for interaction-aware vehicle trajectory prediction

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dc.contributor.authorMo, Xiaoyu
dc.contributor.authorXing, Yang
dc.contributor.authorLv, Chen
dc.date.accessioned2024-11-20T16:05:06Z
dc.date.available2024-11-20T16:05:06Z
dc.date.freetoread2024-11-20
dc.date.issued2024-11
dc.date.pubOnline2024-09-04
dc.description.abstractPredicting the trajectories of neighboring vehicles is vital for self-driving cars in intricate real-world driving. The challenge lies in accounting for diverse influences on a vehicle's movement, travel needs, neighboring vehicles, and a local map. To address these factors comprehensively, we have developed a framework with a Heterogeneous Graph Social (HGS) pooling approach. The framework represents vehicles and infrastructures in a single graph, with vehicle nodes holding historical dynamics information and infrastructure nodes containing spatial features from map images. HGS captures vehicle–infrastructure interactions in urban driving. Unlike existing methods that are restricted to a fixed vehicle count and highway settings, HGS can accommodate variable interactions and road layouts. By merging all features, our approach predicts the target vehicle's future path. Experiments on real-world data confirm HGS's superiority, boasting quicker training and inference, affirming its feasibility, effectiveness, and efficiency.
dc.description.journalNameTransportation Research Part E: Logistics and Transportation Review
dc.description.sponsorshipThis work was supported in part by the Wallenberg-NTU Presidential Postdoctoral Fellowship (Award number: 023485-00001) of Nanyang Technological University, Singapore, the Agency for Science, Technology and Research (A*STAR), Singapore, under the MTC Individual Research Grant (M22K2c0079), the ANR-NRF Joint Grant (No.NRF2021-NRF-ANR003 HM Science), and the Ministry of Education (MOE), Singapore, under the Tier 2 Grant (MOE-T2EP50222-0002).
dc.identifier.citationMo X, Xing Y, Lv C. (2024) Heterogeneous graph social pooling for interaction-aware vehicle trajectory prediction. Transportation Research Part E: Logistics and Transportation Review, Volume 191, November 2024, Article number 103748
dc.identifier.eissn1878-5794
dc.identifier.elementsID553308
dc.identifier.issn1366-5545
dc.identifier.paperNo103748
dc.identifier.urihttps://doi.org/10.1016/j.tre.2024.103748
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23143
dc.identifier.volumeNo191
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S1366554524003399?via%3Dihub
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectTrajectory prediction
dc.subjectConnected vehicles
dc.subjectGraph neural networks
dc.subjectHeterogeneous interactions
dc.subject3509 Transportation, Logistics and Supply Chains
dc.subject35 Commerce, Management, Tourism and Services
dc.subject7 Affordable and Clean Energy
dc.subjectLogistics & Transportation
dc.subject3509 Transportation, logistics and supply chains
dc.titleHeterogeneous graph social pooling for interaction-aware vehicle trajectory prediction
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-08-27

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