Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios

Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, the authors consider the problem of autonomous vehicle control for forced merge scenarios. They propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable leader-follower game. The LFGC estimates the other vehicles’ intentions online based on observed trajectories, and then predicts their future trajectories and plans the ego vehicle’s own trajectory using Model Predictive Control (MPC) to simultaneously achieve probabilistically guaranteed safety and merging objectives. To verify the performance of LFGC, they test it in simulations and with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5% in merging.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3216792
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Liu, Kaiwen
  • Li, Nan
  • Tseng, H Eric
  • Kolmanovsky, Ilya
  • Girard, Anouck
• Publication Date: 2023-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 474-488
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 1
  • 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 vehicles; Merging area; Vehicle trajectories
• Identifier Terms: Model Predictive Control
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01883664
• Record Type: Publication
• Files: TRIS
• Created Date: May 25 2023 5:41PM