TD3強化学習による自動運転車のT字路ナビゲーション
Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation
TD3強化学習を用いた単一エージェント手法で、密集交通下のT字路を安全かつ効率的に走行する自動運転車を実現し、CARLA上で従来手法より遅延・衝突・コストを改善した。
著者: Badr Ben Elallid, Hamza El Alaoui, Nabil Benamar
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.