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A Convolutional Transformer Network for Vehicle Trajectory Prediction in Urban Traffic Scenarios

dc.contributor.authorLi, Anran
dc.contributor.authorXu, Zhenlin
dc.contributor.authorPan, Yuyan “Annie”
dc.contributor.authorZhang, Jian
dc.contributor.authorChen, Ning
dc.contributor.authorYu, Hongsheng
dc.contributor.authorChen, Yanyan
dc.contributor.authorLi, Yongxing
dc.date.accessioned2026-08-20T20:54:22Z
dc.date.available2026-08-20T20:54:22Z
dc.date.copyright2025/11/26
dc.date.issued2026
dc.identifier.otherJTEPBS.TEENG-9065.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313650
dc.description.abstractAbstractMotion optimization for intelligent connected vehicles (ICVs) requires predicting the trajectories of surrounding vehicles to achieve obstacle avoidance, which can be facilitated by the Intelligent Vehicle Cyber-Physical System (IVCPS). IVCPS ...
dc.publisherAmerican Society of Civil Engineers
dc.titleA Convolutional Transformer Network for Vehicle Trajectory Prediction in Urban Traffic Scenarios
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue2
dc.journal.titleJournal of Transportation Engineering, Part A: Systems
dc.identifier.doi10.1061/JTEPBS.TEENG-9065
dc.journal.fristpage04025132-1
dc.journal.lastpage04025132-12
dc.page12
dc.treeJournal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 002
dc.contenttypeFulltext


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