Regional Cooperative Decision-Making Based on Coalition Game for Multilane Merging in Mixed Traffic
Minghao Fu, S Q Li, Mengzhu Guo, Xin Wang, Xin Shane Li, W. Wang · IEEE Transactions on Intelligent Transportation Systems · 2025
The coordinated operation of multiple connected autonomous vehicles (CAVs) is conducive to resolving the bottleneck problem in on-ramp merging areas. However, achieving cooperation under the interference of connected manual vehicles (CMVs) remains challenging. This study integrates lane volume balancing and game theory, developing a regional cooperative decision-making model based on coalition game (RCD-CG) in mixed traffic. First, a progressive cooperation method is proposed, and multiple cooperative regions are designed in the merging area and its upstream section with dynamically adjustable boundaries in response to the time-varying traffic flow. Second, within these regions, CAVs form a coalition to maximize overall efficiency while ensuring safety. Additionally, considering the influence of CMVs outside the coalition on the cooperation of multiple CAVs, the payoffs of CMVs with different driving styles are quantified to solve non-cooperative games between CAVs and CMVs. To validate the model, simulations are conducted on an on-ramp merging scenario with a three-lane mainline, comparing the performance of RCD-CG with other methods across varying CAV penetration, traffic demand, and lane flow unevenness. The results demonstrate that under medium and high CAV penetrations and on-ramp demands, the use of the RCD-CG offers clear benefits in terms of efficiency, with the average speed increase rates reaching 23.2%.