Authority Allocation Strategy for Shared Steering Control Considering Human-Machine Mutual Trust Level

Human-machine mutual trust has become one of the important factors restricting steer-by-wire vehicles due to the removal of mechanical connections. To this end, this article proposes a hierarchical shared steering control framework based on the human-machine mutual trust evaluation. The upper level of the framework aims to evaluate the human-machine mutual trust level. The driver's trust level in the machine is evaluated by the human-machine steering difference. And the machine's trust level in the driver is evaluated by driver skills. The lower level is to dynamically optimize the authority allocation considering varying human-machine mutual trust states. The fuzzy method is adopted to calculate the reference value of the human-machine authority based on the mutual trust level. Through minimizing the lateral acceleration and tracking error, the fuzzy rule database for the authority reference level is further tuned offline. Furthermore, to improve the smoothness of authority transfer and path tracking, the model prediction control is used to optimize the human-machine authority levels online. The proposed authority allocation strategy is verified with the driver-in-the-loop test bench. The results show the effectiveness of improving driving performance under different human-machine mutual trust levels.