A Bargaining Game-Based Human–Machine Shared Driving Control Authority Allocation Strategy
Changhua Dai, Changfu Zong, Dong Zhang, Min Hua, Hongyu Zheng, Chuyo Kaku
Abstract
Research on game-based human-machine shared driving (HMSD) has traditionally been constrained by stringent game theory assumptions, high requirements for automated steering controllers, and the need to acquire the human driver’s target trajectory. To address these issues, this paper proposes a bargaining game-based HMSD control authority allocation strategy for a steer-by-wire vehicle. First, two types of lateral path tracking controllers are developed to simulate the human driver and the automatic steering system. Then, a bargaining game-based HMSD control authority allocation strategy is created using the Nash bargaining solution. Finally, a Simulink/CARSIM co-simulation platform and a driver-in-the-loop experimental bench are utilized to verify the proposed strategy. The results show that the proposed bargaining game-based HMSD control authority allocation strategy has potential advantages over existing game-based HMSD, including decoupling of human-machine interaction, lower requirements for automated steering controllers, and feasible game modeling ability. Moreover, the proposed strategy demonstrates excellent performance in terms of shared driving, which extends the applicability of HMSD in various operating maneuvers. Both the human driver and the automatic steering system benefit from the proposed HMSD system, which could increase the acceptance and trustworthiness of the human driver with the HMSD system and reduce the human driving load to make the driver-vehicle interaction more reliable and smoother.