DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous Driving

• DOI: 10.48550/arxiv.2308.15991
• Publication Date: 2023-01-01
• Authors: Yinda Xu; Lidong Yu
• Keywords: Tracking (education)

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