Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation

• URL: http://arxiv.org/abs/2310.08595v2
• Date: Mon, 16 Oct 2023 09:34:45 GMT
• Title: Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation
• Authors: Badr Ben Elallid, Hamza El Alaoui, and Nabil Benamar
• Abstract summary: Reinforcement learning algorithms have emerged as a promising approach to address these challenges. Here, we address the problem of efficiently and safely navigating T-intersections using a lower-cost, single-agent approach. Our results reveal that the proposed approach enables the AV to effectively navigate T-intersections, outperforming previous methods in terms of travel delays, collision minimization, and overall cost.
• Score: 0.24578723416255746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we explore the challenges associated with navigating complex T-intersections in dense traffic scenarios for autonomous vehicles (AVs). Reinforcement learning algorithms have emerged as a promising approach to address these challenges by enabling AVs to make safe and efficient decisions in real-time. Here, we address the problem of efficiently and safely navigating T-intersections using a lower-cost, single-agent approach based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm. We show that our TD3-based method, when trained and tested in the CARLA simulation platform, demonstrates stable convergence and improved safety performance in various traffic densities. Our results reveal that the proposed approach enables the AV to effectively navigate T-intersections, outperforming previous methods in terms of travel delays, collision minimization, and overall cost. This study contributes to the growing body of knowledge on reinforcement learning applications in autonomous driving and highlights the potential of single-agent, cost-effective methods for addressing more complex driving scenarios and advancing reinforcement learning algorithms in the future.

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