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In this paper, we explore the challenges associated with navigating complexT-intersections in dense traffic scenarios for autonomous vehicles (AVs).Reinforcement learning algorithms have emerged as a promising approach toaddress these challenges by enabling AVs to make safe and efficient decisionsin real-time. Here, we address the problem of efficiently and safely navigatingT-intersections using a lower-cost, single-agent approach based on the TwinDelayed Deep Deterministic Policy Gradient (TD3) reinforcement learningalgorithm. We show that our TD3-based method, when trained and tested in theCARLA simulation platform, demonstrates stable convergence and improved safetyperformance in various traffic densities. Our results reveal that the proposedapproach enables the AV to effectively navigate T-intersections, outperformingprevious methods in terms of travel delays, collision minimization, and overallcost. This study contributes to the growing body of knowledge on reinforcementlearning applications in autonomous driving and highlights the potential ofsingle-agent, cost-effective methods for addressing more complex drivingscenarios and advancing reinforcement learning algorithms in the future.

Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation