Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness

Driving safety is the most important element that needs to be considered for autonomous vehicles (AVs). To ensure driving safety, the authors proposed a lane change decision-making framework based on deep reinforcement learning to find a risk-aware driving decision strategy with the minimum expected risk for autonomous driving. Firstly, a probabilistic-model based risk assessment method was proposed to assess the driving risk using position uncertainty and distance-based safety metrics. Then, a risk aware decision making algorithm was proposed to find a strategy with the minimum expected risk using deep reinforcement learning. Finally, the authors' proposed methods were evaluated in CARLA in two scenarios (one with static obstacles and one with dynamically moving vehicles). The results show that the authors' proposed methods can generate robust safe driving strategies and achieve better driving performances than previous methods.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103452
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21004411
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Li, Guofa
  • Yang, Yifan
  • Li, Shen
  • Qu, Xingda
  • Lyu, Nengchao
  • Li, Shengbo Eben
• Publication Date: 2022-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 134
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Decision making; Driver support systems; Highway safety; Lane changing; Machine learning; Risk
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01790563
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 9 2021 9:11AM