Game-Theoretic Driver-Automation Cooperative Steering Control on Low-Adhesion Roads With Driver Neuromuscular Delay

This paper introduces a novel nonlinear game-based driver-automation cooperative steering control method to mitigate collision caused by the driver's limited experience on low adhesion road conditions. First, we utilize a model predictive control (MPC) driver model to capture the characteristics of driver experience deficit in low adhesion road conditions, considering the driver's neuromuscular delay as the system time lag. Then, a dynamic driving weighting strategy is proposed to adjust the driving weights, taking into account both driver-automation handling conflicts and road risks. Next, in order to account for the nonlinear tire dynamics encountered on low adhesion road surfaces, the problem of driver-automation cooperative steering control is mathematically framed as a nonlinear game. The utilization of the piecewise affine(PWA) theory enables the linearization of the nonlinear game optimization problem, facilitating the derivation of an optimal control strategy for ensuring vehicle stability on low adhesion road conditions. Finally, the proposed method is rigorously validated through simulations and driver-in-the-loop tests, comparing its performance against an existing driver-automation cooperative steering control approach. The experimental results substantiate the effectiveness of the proposed method in mitigating the driver's steering workload and leveraging tire forces optimally to enhance vehicle stability.