Interactive Trajectory Prediction Using a Driving Risk Map-Integrated Deep Learning Method for Surrounding Vehicles on Highways

Accurate trajectory prediction of surrounding vehicles is vital for automated vehicles to achieve high-level driving safety in complex situations. However, most state-of-the-art approaches for multi-vehicle trajectory prediction ignore vehicle motion uncertainty caused by different driving styles. Moreover, the interrelationship between the vehicle and the environment is seldom considered. To address the above problems, this paper proposes a driving risk map-integrated deep learning (DRM-DL) method for interactive trajectory prediction of surrounding vehicles, which comprehensively considers the motion uncertainty, trajectory intention uncertainty and interactions among vehicles, lane lines and road boundaries. Specifically, the authors adopt a conditional variational autoencoder (CVAE) to generate the candidate trajectories, in which the motion uncertainty is considered using a conditional Gaussian distribution. Furthermore, a driving risk map is constructed to realize a unified and interpretable representation of vehicle-vehicle and vehicle-environment interactions. The probability of each candidate trajectory is assigned using a trajectory probability model and a random selection is adopted to select a guided trajectory, which simulates the driver’s trajectory intention uncertainty. Finally, a relearning module is designed to obtain the precise trajectory prediction for surrounding vehicles. The proposed method is evaluated on the HighD dataset, and the results demonstrate a more accurate and reliable trajectory prediction for surrounding vehicles compared with state-of-the-art methods.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3160630
• Record URL:
  https://ieeexplore.ieee.org/document/9745461/
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Liu, Xulei
  • Wang, Yafei
  • Jiang, Kun
  • Zhou, Zhisong
  • Nam, Kanghyun
  • Yin, Chengliang
• Publication Date: 2022-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 19076-19087
• 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: Artificial intelligence; Autonomous vehicle guidance; Traffic safety; Uncertainty; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01869334
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 30 2022 4:58PM