Driver Steering Model Reflecting Personal Characteristics Utilizing Model Predictive Control

This paper proposes a modeling of driver steering operation in lateral vehicle motion control that can be used a reference model to measure the driver intention for driver assistance system, especially aiming to derive a personalized model. In the proposed method, the control law and resulting vehicle motion obtained by applying model predictive control (MPC) to an equivalent two-wheeled vehicle model are utilized as a model representing the standard behavior of the driver. Then a model that reproduces the differences as personal characteristics is constructed and a personal modeling error corrector to reflect the differences as an addition to the MPC system is proposed. By integrating the MPC and the personal modeling error corrector, a driver steering model reflecting personal characteristics is realized. To confirm the effectiveness of the proposed method, driving experiments were conducted on M-sequence and spline interpolation trajectories using a hardware-in-the-loop driving simulator on two experiment participants, one with no driving experience and the other with 35 years of driving experience. Based on the experimental data, the proposed modeling error corrector was used to model the driver's personal characteristics and its performance was evaluated. The results demonstrated that the proposed method can model individual characteristics as a difference from the standard model based on MPC.