Implementation Evaluation of Driver Steering Model Reflecting Personal Characteristics Utilizing Model Predictive Control

This paper presents the implementation and evaluation of a driver steering model that reflects individual driving characteristics by integrating model predictive control (MPC) with a personal modeling error corrector (PMEC). A baseline driver model is first generated using MPC applied to an equivalent two-wheel vehicle model. The deviation between the MPC-based trajectory and the actual driver's trajectory is regarded as an individual characteristic and is modeled using the PMEC. Based on driving data collected from a hardware-in-the-loop (HIL) simulator equipped with a physical steering wheel, the proposed model is identified and its accuracy is assessed through numerical simulation. The model is then deployed in the HIL simulator to reproduce driver steering behavior. Experimental results demonstrate that the proposed model more precisely captures personal driving preferences, such as lane-change timing, compared to MPC alone. Furthermore, the reproduced trajectories closely align with ideal simulations, confirming the HIL simulator as an effective platform for evaluating personalized driving support systems.