A Brief Survey of Deep Reinforcement Learning for Intersection Navigation of Autonomous Vehicles

This paper presents a brief survey of deep reinforcement learning (DRL) for intersection navigation in autonomous driving. Intersection navigation poses significant challenges for autonomous driving (AD), considering the dynamic environment, high complexity, and safety requirements. DRL has emerged as a powerful learning framework in the field of AD, enabling agents to learn interactive driving policies in complex intersection scenarios. The paper begins by outlining the architecture of a general AD system. It then elaborates on the concepts of DRL. Additionally, the paper discusses emerging technologies such as social attention and graph attention mechanisms, which enhance the performance of DRL-based AD driving policies. Finally, the challenges and future prospects are also given. Challenges include complex intersection scenarios, generalization across different layouts, and the lack of standardized benchmarks and evaluation metrics. Overcoming these challenges and leveraging future advancements can lead to improved safety and efficiency in road traffic.