Collision Avoidance Path Planning for Vehicles Combining MPC and CACC Controllers

Abstract

The vehicle collision avoidance system has problems such as poor emergency collision avoidance ability and susceptibility to noise interference. Therefore, this study adopts a vehicle tracking model that integrates dynamic constraints and a two car following model to construct a Collaborative Adaptive Cruise Control (CACC) model that integrates Multi Constraints Model Predictive Control (MPC). And conducted simulation experimental research. The results show that in emergency braking collision avoidance conditions, the collision avoidance model designed in this study can quickly respond to the distance control requirements, and the control stability is higher than that of common collision avoidance systems. The standard deviation of the rear car acceleration of the MPC+CACC, reinforcement learning (RL), and proportional-integral-differential (PID) models are 0.18m/s2, 0.25m/s2, and 0.46m/s2, respectively. The car spacing error of the MPC+CACC, RL, and PID models converge to 0m when the time is about 13 seconds, 33 seconds, and 40 seconds, respectively. This study has certain reference significance for improving the collision avoidance ability of China’s intelligent vehicle collision avoidance system.