A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving

TL;DR

This paper presents a novel autonomous motion planning framework that combines traditional algorithms with reinforcement learning to enhance adaptability, safety, and generalization in complex driving scenarios.

Contribution

It introduces an integrated RL-boosted motion planning system within a Frenet coordinate framework, improving flexibility and safety over traditional methods.

Findings

Significant reduction in collision rates

Enhanced risk management capabilities

Higher goal success rates in diverse scenarios

Abstract

This study introduces a novel approach to autonomous motion planning, informing an analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate system. The combination directly addresses the challenges of adaptability and safety in autonomous driving. Motion planning algorithms are essential for navigating dynamic and complex scenarios. Traditional methods, however, lack the flexibility required for unpredictable environments, whereas machine learning techniques, particularly reinforcement learning (RL), offer adaptability but suffer from instability and a lack of explainability. Our unique solution synergizes the predictability and stability of traditional motion planning algorithms with the dynamic adaptability of RL, resulting in a system that efficiently manages complex situations and adapts to changing environmental conditions. Evaluation of our integrated approach shows a significant reduction in collisions, improved risk management, and improved goal success rates across multiple scenarios. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-RL.