Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient

TL;DR

This paper presents a novel trajectory-tracking control method for autonomous vehicles using the DDPG algorithm within a Frenet coordinate system, enhancing accuracy and stability in complex environments.

Contribution

It introduces a combined approach of Frenet coordinates and DDPG for improved vehicle trajectory tracking, with experimental validation showing superior performance.

Findings

High-precision path tracking achieved in complex environments

Enhanced stability and data efficiency of DDPG with Frenet coordinates

Potential applications in autonomous driving systems

Abstract

This paper studies the application of the DDPG algorithm in trajectory-tracking tasks and proposes a trajectorytracking control method combined with Frenet coordinate system. By converting the vehicle's position and velocity information from the Cartesian coordinate system to Frenet coordinate system, this method can more accurately describe the vehicle's deviation and travel distance relative to the center line of the road. The DDPG algorithm adopts the Actor-Critic framework, uses deep neural networks for strategy and value evaluation, and combines the experience replay mechanism and target network to improve the algorithm's stability and data utilization efficiency. Experimental results show that the DDPG algorithm based on Frenet coordinate system performs well in trajectory-tracking tasks in complex environments, achieves high-precision and stable path tracking, and demonstrates its application potential in autonomous driving and intelligent transportation systems. Keywords- DDPG; path tracking; robot navigation