The paper deals with a robust autonomous path-following functionality, in which the safe velocity and the motion profile of the vehicle with varying tyre-road contact must be guaranteed. The integration of Linear Parameter-Varying (LPV) control and the results of the machine learning-based analysis on the big data of autonomous vehicles is proposed. The integration is achieved in two steps. First, an estimation method on the adhesion coefficient through decision trees is presented. The result of the estimation is incorporated in the robust LPV control through a scheduling variable. During the control design, the error of the machine-learning algorithm is incorporated. Second, an optimisation method of the longitudinal velocity on a predicted horizon is proposed, which is aided with the machine learning-based reachability set approximation of the steering intervention. The effectiveness of the control strategy is illustrated through CarSim simulations.

该论文涉及一种强大的自主路径跟踪功能，其中必须保证具有不同轮胎-道路接触的车辆的安全速度和运动曲线。提出了线性参数变化（LPV）控制与基于机器学习的自动驾驶汽车大数据分析结果的集成。集成分两步完成。首先，提出了一种通过决策树估计粘附系数的方法。估计的结果通过调度变量结合到鲁棒 LPV 控制中。在控制设计过程中，机器学习算法的误差被纳入。其次，提出了一种预测层位纵向速度的优化方法，这与转向干预的基于机器学习的可达性集近似相辅相成。通过 CarSim 仿真说明了控制策略的有效性。