Autonomous driving systems are always built on motion-related modules such as the planner and the controller. An accurate and robust trajectory tracking method is indispensable for these motion-related modules as a primitive routine. Current methods often make strong assumptions about the model such as the context and the dynamics, which are not robust enough to deal with the changing scenarios in a real-world system. In this paper, we propose a Deep Reinforcement Learning (DRL)-based trajectory tracking method for the motion-related modules in autonomous driving systems. The representation learning ability of DL and the exploration nature of RL bring strong robustness and improve accuracy. Meanwhile, it enhances versatility by running the trajectory tracking in a model-free and data-driven manner. Through extensive experiments, we demonstrate both the efficiency and effectiveness of our method compared to current methods.

翻译：自动驾驶系统始终建立在规划器、控制器等运动相关模块之上。对于这些运动相关模块而言，精确且鲁棒的轨迹追踪方法是不可或缺的基础流程。现有方法通常对模型（如环境和动力学特性）做出较强假设，这使得它们在面对真实系统中的动态场景时鲁棒性不足。本文提出一种基于深度强化学习的轨迹追踪方法，用于自动驾驶系统的运动相关模块。深度学习的表征学习能力与强化学习的探索特性相结合，不仅带来了强鲁棒性，还显著提升了精度。同时，该方法通过无模型、数据驱动的轨迹追踪方式增强了通用性。通过大量实验，我们证明了该方法相较于现有方法在效率和有效性上的优势。