用深度强化学习增强车辆横向控制，提升复杂场景下的稳定性与适应性。
Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration
- 结合MPC-PID与深度强化学习，利用实时反馈优化控制策略。
- 在CARLA仿真中验证，即使信息不全也能保持稳定控制性能。
- 演示模块帮助稳定训练，降低自动驾驶系统集成难度。
本文提出一种基于深度强化学习的自动驾驶车辆横向控制方法，尽管车辆模型存在测量误差和简化带来的不完美，仍能实现舒适、高效且鲁棒的控制。控制器由传统的模型预测控制（MPC）-PID部分作为基础与示范器，以及利用MPC-PID实时信息的深度强化学习（DRL）部分组成。在CARLA仿真环境中，以真实路径点为输入评估性能。实验结果表明，当车辆状态信息不完整时，该控制器依然有效；同时，示范器有助于稳定DRL训练过程。研究结果展示了未来降低自动驾驶系统开发与集成成本的潜力。
原文摘要 · Abstract (English)
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.
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