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

TL;DR

This paper introduces a DRL-based trajectory tracking method for autonomous driving that enhances robustness, accuracy, and versatility by leveraging deep learning and reinforcement learning, outperforming existing methods in various scenarios.

Contribution

The paper presents a novel model-free, data-driven DRL approach for trajectory tracking in autonomous driving, addressing robustness and adaptability issues of traditional methods.

Findings

Demonstrates improved accuracy over traditional methods

Shows robustness in changing driving scenarios

Provides efficient and effective trajectory tracking results

Abstract

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.