Interaction-Aware Decision-Making for Automated Vehicles Using Social Value Orientation

Motion control algorithms in the presence of pedestrians are critical for the development of safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on manually designed decision-making policies which neglect the mutual interactions between AVs and pedestrians. On the other hand, recent advances in Deep Reinforcement Learning allow for the automatic learning of policies without manual designs. To tackle the problem of decision-making in the presence of pedestrians, the authors introduce a framework based on Social Value Orientation and Deep Reinforcement Learning (DRL) that is capable of generating decision-making policies with different driving styles. The policy is trained using state-of-the-art DRL algorithms in a simulated environment. A novel computationally-efficient pedestrian model that is suitable for DRL training is also introduced. The authors perform experiments to validate their framework and they conduct a comparative analysis of the policies obtained with two different model-free Deep Reinforcement Learning Algorithms. Simulations results show how the developed model exhibits natural driving behaviours, such as short-stopping, to facilitate the pedestrian's crossing.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2022.3189836
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Crosato, Luca
  • Shum, Hubert P H
  • Ho, Edmond S L
  • Wei, Chongfeng
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1339-1349
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 8
  • 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: Algorithms; Autonomous vehicles; Decision making; Driving behavior; Machine learning; Pedestrian movement; Pedestrian safety; Pedestrian vehicle interface; Social values; Traffic safety; Trajectory control
• Subject Areas: Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors; Society; Vehicles and Equipment;

Filing Info

• Accession Number: 01884252
• Record Type: Publication
• Files: TRIS
• Created Date: May 31 2023 10:58AM