Game Theory-Based Coordinated Control Strategy for Multiple Types of Vehicles in Ramp Concourse Area

Decision modeling based on game theory provides an effective means to achieve safe and efficient ramp merging. However, there are some limitations in the current research, such as previous ramp merge control only studied the interaction problem of networked autonomous vehicles, ignoring the diversity of vehicle types, which is a non-negligible problem in real life. To solve this problem, this study proposes to use different game approaches to address the merging challenge. First, a static game is used to deal with the merging problem of networked self-driving vehicles, and then a belief pool with non-cooperative game approach is used to deal with the problem of human driver’s driving style with the merging problem of self-driving vehicles with human-driven vehicles with unknown information. The simulation results show that the efficiency of on-ramp merging can be significantly improved when networked self-driving cars interact with each other; in the case of merging self-driving cars with human-driven cars, the self-driving cars can recognize the driving styles of the opposite cars and make accurate decisions, which improves the driving efficiency, ensures driving safety and maximizes passenger comfort to the greatest extent.