Lane-Changing Trajectory Tracking and Simulation of Autonomous Vehicles Based on Model Predictive Control
Hui Song, Dayi Qu, Haibing Guo, Kekun Zhang, Tao Wang
Abstract
In order to realize the lane-changing maneuver of Connected Autonomous Vehicles (CAV), a lateral controller based on model predictive control is developed with the three degrees of freedom vehicle dynamic model. Then the controller is synthesized to track the reference trajectory fitted by the quintic Bézier curve. The controller is validated by MATLAB/CarSim under different road adhesion conditions and driving speeds. Results show that for different road adhesion conditions and driving speeds, the controller does not need to adjust the control parameters and can continuously correct the deviation from the expected trajectory. During the tracking process, the front wheel angle, front wheel angle increment, centroid side deflection angle, and front wheel side deflection angle are kept within the limited constraint range. The established control algorithm has good control robustness and tracking driving stability. The research can provide a theoretical basis and technical support for lane-changing safety and control of CAV.