Adaptive Cruise Control with Motion Sickness Reduction: Data-driven Human Model and Model Predictive Control Approach

Abstract

This paper proposes Adaptive Cruise Control (ACC) to reduce Motion Sickness (MS). A human model is obtained from real-world experimental data to predict human motion. Motion Sickness Dose Value is calculated from the human motion data to evaluate motion sickness. Model Predictive Control (MPC) is used to obtain the optimal control under a multi-objective cost function and constraints. With the satisfaction of constraints, collision avoidance and reduction of MS are obtained. The simulation results confirm that the proposed method reduces MS compared to other methods, e.g., general ACC and MPC.