基于优化预瞄距离的无人驾驶车辆轨迹跟踪控制

Trajectory Tracking Control of Autonomous Vehicle Based on Optimized Preview Distance

摘要:

实现快速、准确的轨迹跟踪控制是自动驾驶技术发展的核心任务。为此，本文建立了车辆预瞄偏差动力学模型，基于模型预测控制算法构建了以前轮转角为控制量的轨迹跟踪控制器，以实现结合车辆状态和参考轨迹设计预瞄距离优化方法，提高跟踪精度。综合考虑车辆跟踪控制的准确性和行驶稳定性设计目标函数与约束条件，搭建Carsim-Simulink联合仿真平台，验证算法的有效性，在双移线工况下进行不同车速的仿真试验，探索预瞄距离对控制结果的影响并设计预瞄距离优化方法。结果表明：与无预瞄距离优化相比，本文设计的轨迹跟踪控制器能够实现高精度控制，在车辆中高速行驶时，横向位移平均误差小于10 cm，航向角平均误差小于1°，并使车辆稳定性显著提升。

Abstract:

Developing a fast and accurate trajectory tracking method is a core control task in the development of autonomous driving technology. This paper establishes a dynamic residual model based on the vehicle preview distance and constructs a trajectory tracking controller using a model predictive control （MPC） algorithm with the front wheel steering angle as the control input. By designing an optimized preview distance method that considers the vehicle state and the reference trajectory， the tracking accuracy is improved. The objective function and constraint conditions are designed to comprehensively consider the accuracy of vehicle tracking control and driving stability. A jiont simulation platform combing Carsim and Simulink is built to verify the effectiveness of the algorithm. Conduct simulation experiments at different speeds under double lane change conditions to explore the impact of preview distance on control results and design optimization methods for preview distance. The results of the simulation experiments demonstrate that compared to the case without optimized preview distance， the trajectory tracking controller designed in this paper can achieve high-precision control. When the vehicle is traveling at high speed， the lateral displacement error is less than 10 cm， the heading angle error is less than 1°， and the vehicle stability is significantly improved.