Interactive Planning for Autonomous Driving in Intersection Scenarios Without Traffic Signs

Efficient intersection planning is one of the most challenging tasks for an autonomous vehicle at present. Politeness to other traffic participants and reaction to surrounding information are the key aspects that determine the performance of planning algorithm for an autonomous vehicle. In order to capture these aspects, the authors propose a planning framework, based on the partially observable Markov decision process (POMDP), to ensure social compliance and optimize the motion response of autonomous vehicles. The framework designs a novel POMDP model to ensure effective prediction of behavioral intentions and decision-making at intersections with no traffic signs and low traffic volume. Then, a deterministic planner is employed as the baseline to realize planning. Specifically, they consider both the driving position and the facing angle of the vehicle in real-world situations. At the same time, they utilize scattering methods for probability updating and intent determination to improve the algorithm’s adaptability to real-world scenarios. The proposed framework is evaluated in different scenarios to demonstrate its capabilities in terms of the interactive planning for an autonomous vehicle.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3205250
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Xia, Canming
  • Xing, Mali
  • He, Shenghuang
• Publication Date: 2022-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 24818-24828
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 12
  • 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; Decision making; Intersections; Planning
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01876298
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 21 2023 9:27AM