MPC-PF: Socially and Spatially Aware Object Trajectory Prediction for Autonomous Driving Systems Using Potential Fields

Predicting object motion behaviour is a challenging but crucial task for safe decision making and path planning for autonomous vehicles. It is challenging in large part due to the uncertain, multi-modal, and practically intractable set of possible agent-agent and agent-space interactions, especially in urban driving settings. Models solely based on constant velocity or social force have an inherent bias and may lead to inaccurate predictions across the prediction horizon whereas purely data driven approaches suffer from a lack of holistic set of rules governing predictions. The authors tackle this problem by introducing MPC-PF: a novel potential field-based trajectory predictor that incorporates social interaction via agent-agent and agent-space considerations and is able to tradeoff between inherent model biases across the prediction horizon. Through evaluation on the Waymo Open Motion Dataset and a variety of other common urban driving scenarios, they show that their model is capable of achieving state-of-the-art performance while producing accurate predictions for both short and long term timesteps. They also demonstrate the significance of their model architecture through an ablation study.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2023.3243004
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Bhatt, Neel P
  • Khajepour, Amir
  • Hashemi, Ehsan
• Publication Date: 2023-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 5351-5361
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 5
  • 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: Autonomous vehicle guidance; Predictive models; Trajectory control; Urban highways; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01893951
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 22 2023 9:06AM