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. Code and documentation are released to facilitate both further research and industrial deployment.

翻译：自动驾驶系统始终建立在规划器与控制器等运动相关模块之上。准确且鲁棒的轨迹跟踪方法作为基础例程对这些运动相关模块不可或缺。现有方法通常对模型做出上下文与动力学等强假设，在处理真实系统中不断变化的场景时鲁棒性不足。本文提出一种基于深度强化学习的自动驾驶系统运动相关模块轨迹跟踪方法。深度学习强大的表征学习能力与强化学习的探索特性共同保证了强鲁棒性与精度提升。同时，通过以无模型、数据驱动方式执行轨迹跟踪增强了方法的通用性。通过广泛实验，我们证明了该方法相比现有方法兼具高效性与有效性。相关代码与文档已发布，以促进后续研究与工业部署。