Human–Machine Shared Steering Control Under High-Speed Emergency Obstacle Avoidance Scenarios

Abstract

The allocation of controlling and driving authority is an important and difficult aspect of human–machine shared steering control (SSC). This paper addresses the SSC problem in high-speed emergency obstacle avoidance scenarios. A parallel SSC framework containing a dynamic driving authority allocation model and a path tracking controller is developed, where the human driver and controller can control the vehicle simultaneously. First, fuzzy logic is adopted in the SSC framework to actively adjust the driving authority between the human driver and the controller. The driver steering state and path tracking error are considered to reduce the negative impact of a driver’s mis-operation and weaken any human–machine conflict. Subsequently, the path tracking controller in the proposed SSC system is designed based on a nonlinear vehicle lateral model to improve the accuracy of the controller, particularly when the vehicle is facing large lateral acceleration. To address the nonlinear control problem, the Udwadia–Kalaba approach is employed and the Lyapunov stability of the controller is proved. Finally, the effectiveness of the proposed SSC system is proved through simulation results, which show that the vehicle has excellent path tracking performance in high-speed obstacle avoidance scenarios. In addition, the system can resolve the human–machine conflict problem.