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

Autonomous driving has garnered significant attention Reinforcement Learning (RL) has become a powerful in recent years, especially in optimizing vehicle approach for addressing complex decision-making performance under varying conditions. This paper challenges in autonomous systems, particularly in addresses the challenge of maintaining maximum low-speed scenarios. Unlike high-speed driving, lowspeed speed stability in low-speed autonomous driving environments demand high precision, safety, while following a predefined route. Leveraging and stability [7] due to dynamic obstacles and confined reinforcement learning (RL), we propose a novel approach spaces. This paper explores several applications to optimize driving policies that enable the of RL in low-speed contexts, demonstrating its potential vehicle to achieve near-maximum speed without compromising to enhance performance in various tasks.