Model Predictive Control Approach to Design Practical Adaptive Cruise Control for Traffic Jam

摘要

This paper presents a design method of a Model Predictive Control (MPC) with low computational cost for a practical Adaptive Cruise Control (ACC) running on an embedded microprocessor. Generally, a problem with previous ACC is slow following response in traffic jams, in which stop-and-go driving is required. To improve the control performance, it is important to design a controller considering vehicle characteristics which significantly changes depending on driving conditions. In this paper, we attempt to solve the problem by using MPC that can explicitly handle constraints imposed on, e.g., actuator or acceleration response. Furthermore, we focus on decreasing the computational load for the practical use of MPC by using low-order prediction model. From these results, we developed ACC with high responsiveness and less discomfort even for traffic jam scene.