Modeling Driver Steering Control Based on Stochastic Model Predictive Control

Simulation-based vehicle system design and development of various active chassis control systems necessitate an enhanced understanding of driver-vehicle systems, in particular improved modeling of driver driving control characteristics. A number of research efforts have been made in developing driver models in the past few decades. However, little effort has been attempted in modeling driver steering control behavior capturing vehicle-road system parameter uncertainties. In this paper, a novel driver steering control model based on stochastic model predictive control (SMPC) is proposed to effectively incorporate the variations in the vehicle-road system parameters. The proposed SMPC-based driver steering control framework consists of three modules, namely perception, decision and execution, where a multi-point driver preview approach is employed. An internal vehicle dynamics model with the parameter uncertainty in road friction coefficient is formulated to represent the driver´s knowledge and adaptation about the variations in road conditions. The SMPC method is then used to minimize a cost function that is a weighted combination of lateral path error and ease of driver control. Simulation analysis about the variant parameters and comparison with an MPC-based driver model demonstrate the effectiveness and robustness of the proposed SMPC-based driver steering control model.