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：自动驾驶系统始终构建于规划器和控制器等运动相关模块之上。作为基础性功能模块，精确鲁棒的轨迹跟踪方法对这些运动相关模块不可或缺。现有方法通常对模型（如上下文和动力学特性）做出强假设，难以鲁棒应对真实系统不断变化的场景。本文提出一种基于深度强化学习的轨迹跟踪方法，用于自动驾驶系统中的运动相关模块。深度学习强大的表征学习能力与强化学习的探索特性相结合，带来了强鲁棒性并提升了精度。同时，该方法以无模型、数据驱动的方式运行轨迹跟踪，增强了通用性。通过大量实验，我们证明了该方法相比现有方法在效率和有效性方面的优势。