Model predictive control is one of the main methods used in path tracking for autonomous vehicles. To improve the path tracking performance of the vehicle, a path tracking method based on model predictive control with variable predictive horizon is proposed in this paper. Based on the designed model predictive controller for path tracking, the response analysis of path tracking control system under the different predictive horizons is carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. Then, taking the lateral offset, the steering frequency and the real-time of the control algorithm as comprehensive performance indexes, the particle swarm optimization algorithm is designed to realize the adaptive optimization for the predictive horizon. The effectiveness of the proposed method is evaluated via numerical simulation based on Simulink/CarSim and hardware-in-the-loop experiment on an autonomous driving simulator. The obtained results show that the optimized predictive horizon can adapt to the different driving environment, and the proposed path tracking method has good comprehensive performance in terms of path tracking accuracy of the vehicle, driving comfort and real-time.

模型预测控制是用于自动驾驶车辆的路径跟踪的主要方法之一。为了提高车辆的路径跟踪性能，提出了一种基于具有可变预测水平的模型预测控制的路径跟踪方法。基于设计的路径跟踪模型预测控制器，对不同预测水平下的路径跟踪控制系统进行了响应分析，以明确预测水平对路径跟踪精度，驾驶舒适性和控制算法实时性的影响。然后，以横向偏移，转向频率和控制算法的实时性为综合性能指标，设计了粒子群优化算法，实现了对预测层的自适应优化。通过基于Simulink / CarSim的数值模拟以及在自动驾驶模拟器上进行的硬件在环实验，评估了该方法的有效性。获得的结果表明，优化的预测视野能够适应不同的驾驶环境，并且所提出的路径跟踪方法在车辆的路径跟踪精度，驾驶舒适性和实时性方面都具有良好的综合性能。