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

• URL: http://arxiv.org/abs/2412.16248v1
• Date: Fri, 20 Dec 2024 01:06:41 GMT
• Title: Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed
• Abstract summary: This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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