Using Reachable Sets for Trajectory Planning of Automated Vehicles

The computational effort of trajectory planning for automated vehicles often increases with the complexity of the traffic situation. This is particularly problematic in safety-critical situations, in which the vehicle must react in a timely manner. The authors present a novel motion planning approach for automated vehicles, which combines set-based reachability analysis with convex optimization to address this issue. This combination makes it possible to find driving maneuvers even in small and convoluted solution spaces. In contrast to existing work, the computation time of the authors' approach typically decreases, the more complex situations become. The authors demonstrate the benefits of their motion planner in scenarios from the CommonRoad benchmark suite and validate the approach on a real test vehicle.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2020.3017342
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Manzinger, Stefanie
  • Pek, Christian
  • Althoff, Matthias
• Publication Date: 2021-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 232-248
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 6
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Optimization; Test vehicles; Trajectory control; Validation
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01779796
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 25 2021 11:42AM