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.