Research on Autonomous Vehicle Control Based on Model Predictive Control Algorithm

In order to improve control smoothness, this paper constructs a five-module model based on model predictive control algorithm, which integrates sensing, communication and decision modes, and focuses on solving the control problem of autonomous vehicles. Firstly, the decision logic of signal light is studied, and the switching mode of signal light is designed by state machine model. Secondly, the dynamics model of the autonomous vehicle is constructed, and the trajectory tracking is optimized through the MPC design control input, and the ride comfort and handling stability of the vehicle are analyzed. Finally, a five-module model is established for overall integration, and simulation experiments are carried out for intersection and autonomous vehicle under straight line driving, stopping and following state. The results show that the proposed collaborative optimization method significantly improves the efficiency of the traffic system and reduces the energy consumption, which verifies the effectiveness of the experimental platform.