DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous Driving
This addresses the need for more robust trajectory tracking in autonomous driving systems, which is incremental as it applies DRL to a known bottleneck.
The paper tackles the problem of accurate and robust trajectory tracking for motion-related modules in autonomous driving by proposing a Deep Reinforcement Learning (DRL)-based method, demonstrating improved efficiency and effectiveness compared to current methods through extensive experiments.
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.