Human–Machine Cooperative Steering Control Considering Mitigating Human–Machine Conflict Based on Driver Trust

To reduce the impact of human–machine conflict on vehicle safety, this study proposes a novel human–machine cooperative steering control approach from the perspective of driver trust in the machine. The relationship between driver trust in the machine and driving skill is analyzed by the chi-square test method, and an online cooperative algorithm is designed using fuzzy control for different conditions, which assigns control authority based on driver trust under safe conditions and gives most of the authority to the machine to ensure safety under dangerous conditions. The machine is designed using model predictive control as an alternative controller parallel to the driver. To implement the proposed approach, a simulation platform that includes drivers and a test vehicle is established. Based on the driving data of human drivers collected in field tests, a two-point visual driver model is established to simulate steering behaviors and reflect physical workload. The parameters of the driver model are identified by a particle swarm optimization method to represent different drivers. The effectiveness of the approach, such as guaranteeing vehicle safety and reducing physical workload and human–machine conflict, is verified by simulations under typical conditions and obstacle avoidance conditions based on veDYNA vehicle dynamics software.