Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed

TL;DR

This paper introduces a reinforcement learning-based method to enhance route stability and maximize speed in low-speed autonomous driving, ensuring safety and accuracy.

Contribution

It presents a novel RL approach specifically designed for low-speed scenarios to improve speed stability and route adherence in autonomous vehicles.

Findings

Achieved near-maximum speed in low-speed driving conditions

Maintained high route accuracy and safety standards

Demonstrated effectiveness of RL in optimizing low-speed driving policies

Abstract

Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (RL), we propose a novel approach to optimize driving policies that enable the vehicle to achieve near-maximum speed without compromising on safety or route accuracy, even in low-speed scenarios.