Cooperative decision-making of multiple autonomous vehicles in a connected mixed traffic environment: A coalition game-based model

Advances in vehicle-networking technologies have enabled vehicles to cooperate in mixed traffic. However, realizing the cooperative decision-making of multiple connected autonomous vehicles (CAVs) when influenced by the presence of connected manual vehicles (CMVs) is a challenging area in current research. In this study, the authors propose a coalition game-based (CG-based) model for multi-CAV cooperative decision-making in a connected mixed traffic environment. First, the model integrates the perceived risk field theory, quantifying the driving risk from the perspective of different CMVs; this risk is used to determine the uncertainty of the motion state of CMVs. Second, the model can identify the conflicts caused by multiple lane-changing vehicles and decouple the conflict problem into multiple two-vehicle lane-changing games, including a cooperative game between two CAVs and a non-cooperative game between a CAV and a CMV. To test the proposed model, four scenarios that blocked the passage of multiple CAVs were set up; in these scenarios, the average speed of the CG-based model was 21.05, 16.76, 23.17, and 12.55% higher than that of the LC2013 model. The simulation results showed that the CG-based model could improve the efficiency of multiple CAVs while ensuring safety in a mixed traffic flow.

• Record URL:
  https://doi.org/10.1016/j.trc.2023.104415
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X23004059
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Fu, Minghao
  • 0000-0001-8913-6497
  • Li, Shiwu
  • Guo, Mengzhu
  • Yang, Zhifa
  • Sun, Yaxing
  • Qiu, Chunxiang
  • Wang, Xin
  • Li, Xin
• Publication Date: 2023-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 104415
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 157
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Decision making; Game theory; Uncertainty; Vehicle mix
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01900686
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 28 2023 10:37AM