标题： 使用MPC-PID演示的基于深度强化学习的自动驾驶汽车横向控制 显示英文标题

标题： Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration

摘要： 控制器是自动驾驶流水线中最重要的模块之一，确保车辆到达期望位置。 在这项工作中，提出了一种基于强化学习的横向控制方法，尽管由于测量误差和简化导致车辆模型存在不完善之处，该方法仍能提供舒适的、高效的和鲁棒的控制性能，同时考虑了控制与其他模块之间的接口。 该控制器由传统的基于模型预测控制（MPC）-PID部分作为基础，并结合演示器以及利用MPC-PID部分在线信息的深度强化学习（DRL）部分组成。 控制器的性能在CARLA中使用航路点的真实值作为输入进行了评估。 实验结果表明，当车辆信息不完整时，控制器的有效性得到了证明，并且DRL的训练可以通过演示部分得到稳定。 这些发现突显了未来减少自动驾驶流水线开发和集成工作量的潜力。 显示英文摘要

摘要： The controller is one of the most important modules in the autonomous driving pipeline, ensuring the vehicle reaches its desired position. In this work, a reinforcement learning based lateral control approach, despite the imperfections in the vehicle models due to measurement errors and simplifications, is presented. Our approach ensures comfortable, efficient, and robust control performance considering the interface between controlling and other modules. The controller consists of the conventional Model Predictive Control (MPC)-PID part as the basis and the demonstrator, and the Deep Reinforcement Learning (DRL) part which leverages the online information from the MPC-PID part. The controller's performance is evaluated in CARLA using the ground truth of the waypoints as inputs. Experimental results demonstrate the effectiveness of the controller when vehicle information is incomplete, and the training of DRL can be stabilized with the demonstration part. These findings highlight the potential to reduce development and integration efforts for autonomous driving pipelines in the future.