This paper proposes an adaptable path tracking control system based on Reinforcement Learning (RL) for autonomous cars. A four-parameter controller shapes the behavior of the vehicle to navigate on lane changes and roundabouts. The tuning of the tracker uses an educated Q-Learning algorithm to minimize the lateral and steering trajectory errors. The CARLA simulation environment was used both for training and testing. The results show the vehicle is able to adapt its behavior to the different types of reference trajectories, navigating safely with low tracking errors. The use of a ROS bridge between the CARLA and the tracker results (i) in a realistic system, and (ii) simplifies the replacement of the CARLA by a real vehicle. An argument on the dependability of the overall architecture based on stability results of non-smooth systems is presented at the end of the paper.

翻译：本文提出了基于自发汽车强化学习(RL)的可调整路径跟踪控制系统。 4参数控制器塑造了车辆在车道变化和环形上行驶的行为。 跟踪器的调试使用了受过教育的Q- 学习算法以尽量减少横向和方向轨道错误。 CARLA模拟环境既用于培训和测试。 结果表明,该车辆能够使其行为适应不同类型的参考轨迹,安全地航行,跟踪错误低。 CARLA与跟踪器结果(i) 在现实系统中使用ROS桥(i) 和跟踪器结果(ii) 简化用真车替换CARLA。 论文结尾处介绍了基于非光谱系统稳定性结果的总体结构的可依赖性。