@article{ref1,
title="Cooperative decision-making of multiple autonomous vehicles in a connected mixed traffic environment: a coalition game-based model",
journal="Transportation research part C: emerging technologies",
year="2023",
author="Fu, Minghao and Li, Shiwu and Guo, Mengzhu and Yang, Zhifa and Sun, Yaxing and Qiu, Chunxiang and Wang, Xin and Li, Xin",
volume="157",
number="",
pages="e104415-e104415",
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.<p /> <p>Language: en</p>",
language="en",
issn="0968-090X",
doi="10.1016/j.trc.2023.104415",
url="http://dx.doi.org/10.1016/j.trc.2023.104415"
}