Driving Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections: A Differential Game Approach
Peng Hang, Chao Huang, Zhongxu Hu, Chen Lv
Abstract
Considering personalized driving preferences, a new decision-making framework is developed using a differential game approach to resolve the driving conflicts of autonomous vehicles (AVs) at unsignalized intersections. To realize human-like driving and personalized decision-making, driving aggressiveness is first defined for AVs. To improve driving safety, a Gaussian potential field model is built for collision risk assessment. Besides, in the proposed decision-making framework, the collision risk assessment model is further used to reduce the computational complexity based on an event-triggered mechanism. In the construction of payoff function, both driving safety and passing efficiency are comprehensively considered, and the driving aggressiveness is also reflected. Two kinds of equilibrium solution to the differential game, i.e., the Nash equilibrium and Stackelberg equilibrium, are discussed and solved. Finally, the proposed decision-making algorithm is tested through a hardware-in-the-loop testing platform, and its feasibility, effectiveness, and real-time implementation performance are validated.