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arXiv 2301.03363eess.SYcs.AIcs.SY
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使用强化学习调整自动驾驶汽车的路径跟踪控制器

Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning

Instituto Superior Tecnico, University of Lisbon(里斯本大学高等技术学院);Institute for Systems and Robotics(系统与机器人研究所)

Ana Carrasco, João Sequeira

摘要:

提出了一种基于强化学习的自适应路径跟踪控制系统。四参数控制器塑造车辆的行为,以在车道变换和绕行时导航。跟踪器的调整使用受过教育的Q学习算法来最小化横向和转向轨迹误差。CARLA模拟环境用于培训和测试。结果表明,车辆能够适应不同类型的参考轨迹,安全导航,跟踪误差低。在CARLA和跟踪器之间使用ROS桥导致(i)在现实系统中,以及(ii)简化了用真实车辆替换CARLA。基于非光滑系统的稳定性结果的整体架构的可靠性的论点,提出在文件的最后。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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