Learning the Subsystem of Local Planning for Autonomous Racing

TL;DR

This paper introduces a hierarchical planning system for autonomous racing that uses reinforcement learning for obstacle avoidance without needing an obstacle map, achieving faster lap times and high success rates.

Contribution

The paper presents a novel hierarchical architecture with a reinforcement learning-based obstacle avoidance subsystem that operates without an obstacle map, improving racing performance.

Findings

Achieves faster average lap times than end-to-end baselines.

Maintains a 94% success rate in obstacle avoidance.

Operates effectively with only 10 laser range finders.

Abstract

The problem of autonomous racing is to navigate through a race course as quickly as possible while not colliding with any obstacles. We approach the autonomous racing problem with the added constraint of not maintaining an updated obstacle map of the environment. Several current approaches to this problem use end-to-end learning systems where an agent replaces the entire navigation pipeline. This paper presents a hierarchical planning architecture that combines a high level planner and path following system with a reinforcement learning agent that learns that subsystem of obstacle avoidance. The novel "modification planner" uses the path follower to track the global plan and the deep reinforcement learning agent to modify the references generated by the path follower to avoid obstacles. Importantly, our architecture does not require an updated obstacle map and only 10 laser range finders to avoid obstacles. The modification planner is evaluated in the context of F1/10th autonomous racing and compared to a end-to-end learning baseline, the Follow the Gap Method and an optimisation based planner. The results show that the modification planner can achieve faster average times compared to the baseline end-to-end planner and a 94% success rate which is similar to the baseline.