Citation

Fu M, Li S, Guo M, Yang Z, Sun Y, Qiu C, Wang X, Li X. Transp. Res. C Emerg. Technol. 2023; 157: e104415.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trc.2023.104415

PMID

unavailable

Abstract

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, we 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.

Language: en

Keywords

Coalition game; Connected autonomous vehicles (CAVs); Connected mixed traffic environment; Cooperative decision-making; Perceived risk