Human–Machine Shared Control for Industrial Vehicles: A Personalized Driver Behavior Recognition and Authority Allocation Scheme

Abstract

Human-machine shared control has become a transitional solution from manual driving to autonomous driving, attracting increasing attention. In order to effectively enhance the stability of the vehicle and achieve adaptive collaborative control between the machine and human driver with different operating behaviors, this paper proposes a novel personalized steering behavior recognition method. On this basis, an adaptive authority allocation scheme is designed to alleviate the workload of the drivers and improve vehicle stability. Firstly, to identify different driving styles accurately, the K-means algorithm is modified based on the adaptive variation of weight coefficients. Subsequently, the numerical functions, considering tracking accuracy and steering error, are utilized to individually develop the reference models for various steering behaviors. Further optimal allocation problems involving driving workload, tracking performance, authority smoothness, and stability are addressed through MPC. Simulation results indicate that the proposed driver recognition method can distinguish ambiguous driving behaviors, and the shared steering control strategy demonstrates superior performance in path tracking, stability enhancing, and driver workload alleviating.