A Two-Layer Potential-Field-Driven Model Predictive Shared Control Towards Driver-Automation Cooperation

This paper proposes a novel driver-automation shared control based on a potential-field-driven model predictive controller (PF-MPC) and a two-layer fuzzy strategy (TLFS) to address driver-automation conflicts and control authority allocation issues. The PF-MPC approach based on the driver-vehicle model is introduced to deal with obstacles avoidance and driver-automation conflicts. The potential field is constructed to evaluate the driving risk by considering the driving environment and vehicle states, meanwhile, it is also involved in the optimized objective in the PF-MPC controller for obstacle avoidance. The tuning weight is designed to adjust the trade-off between the motion planning-related cost and driver-related cost to reduce driver-automation conflicts. To further alleviate the conflict and control authority allocation between the human driver and PF-MPC controller, the TLFS for shared control is designed based on the evaluation of the driving risk level and conflict situation, and the values of the tuning weight and cooperative coefficient are determined using the fuzzy control method. Moreover, comparative studies are conducted to verify the driving safety and conflict management performance of the proposed shared control method on a straight road and a curvy road. The results show that the proposed shared control method can help drivers avoid obstacles safely and alleviate the driver-automation conflicts in different driving conditions.