Research on lane-changing decision-making model for autonomous driving cars based on game theory

In order to ensure the safe lane changing of self-driving vehicles and at the same time improve the efficiency of vehicle traffic, a game theory-based vehicle lane changing decision model is proposed. Firstly, the interaction behavior and game relationship between vehicles are analyzed. Then the safety gain, comfort gain and speed gain are established as the game gain function, and the vehicle driving conditions in the NGSIM dataset are clustered and analyzed using Gaussian mixture model (GMM), which divides the driver styles into three categories. The anthropomorphism of the vehicle lane change decision model is improved by considering the effects of different driving styles on the vehicle lane change decision. Finally, SUMO software is used to build a hybrid scenario of human-driven vehicles and self-driven vehicles to compare and analyze with the MOBIL lane change model. The results show that the lane-changing game decision-making model in this paper has higher safety and traffic efficiency.