Adaptive Cruise Control Based on a Model Predictive Controller Considering the Driving Behavior of the Front and Rear Vehicles

Abstract

Aiming to improve the lateral instability of adaptive cruise control (ACC) systems, both the front and rear vehicles are considered the centers of two control strategies. A vehicle control system is designed to enable the vehicle to automatically find the best following distance based on the displacement and speeds of the front and rear vehicles, hence enhancing driver assistance, traffic efficiency, and road utilization ratio. A practical model predictive control is designed to improve performance, responsiveness, and minor discomfort. A quadratic programming (QP) solver is used to construct an error preview-based mathematical model for the vehicle control, which is then applied to improve the control performance of the system to achieve relative intervehicle distance control. The time sampling of the parameters and the prediction horizon are obtained by numerical simulation, verifying the effectiveness of the ACC system proposed.