Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles spatial interactions using a graph convolutional network (GCN), and captures temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, the authors propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of the scheme. The network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM). Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that the network can achieve state-of-the-art performance.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3155749
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Sheng, Zhengguo
  • Xu, Yanwu
  • 0000-0002-7973-3316
  • Xue, S
  • Li, D
• Publication Date: 2022-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 17654-17665
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 10
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Automated vehicle control; Autonomous vehicles; Freeways; Traffic safety; Vehicle mix; Vehicle trajectories
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01866544
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 5 2022 3:10PM