VRR-Net: Learning Vehicle–Road Relationships for Vehicle Trajectory Prediction on Highways

Tingzhang Zhan, Qieshi Zhang, Guangxi Chen and Jun Cheng ()
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Tingzhang Zhan: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Qieshi Zhang: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Guangxi Chen: School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
Jun Cheng: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Mathematics, 2023, vol. 11, issue 6, 1-15

Abstract: Vehicle trajectory prediction is an important decision-making and planning basis for autonomous driving systems that enables them to drive safely and efficiently. To accurately predict vehicle trajectories, the complex representations and dynamic interactions among the elements in a traffic scene are abstracted and modelled. This paper presents vehicle–road relationships net, a deep learning network that extracts features from vehicle–road relationships and models the effects of traffic environments on vehicles. The introduction of geographic highway information and the calculation of spatiotemporal distances with a reference not only unify heterogeneous vehicle–road relationship representations into a time series vector but also reduce the requirement for sensing transient changes in the surrounding area. A hierarchical long short-term memory network extracts environmental features from two perspectives—social interactions and road constraints—and predicts the future trajectories of vehicles by their manoeuvre categories. Accordingly, vehicle–road relationships net fully exploits the contributions of historical trajectories and integrates the effects of road constraints to achieve performance that is comparable to or better than that of state-of-the-art methods on the next-generation simulation dataset.

Keywords: trajectory prediction; spatiotemporal awareness; spatiotemporal graph; vehicle–road relationships (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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