Edge-enhanced Graph Attention Network for driving decision-making of autonomous vehicles via Deep Reinforcement Learning

Despite the rapid advancement in the field of autonomous driving vehicles, developing a safe and sensible decision-making system remains a challenging problem. The driving decision-making module is one of the most essential sections of the entire autonomous driving system, and the decision generated from it can significantly impinge the lives and property of passengers. Complicated interactions among traffic participants have the most profound impact on the decision-making process, yet the interactions are often simplified or overlooked due to their complexity and implicit nature. To address this issue, this work proposes an Edge-Enhanced Graph Attention Reinforcement Learning (EGARL) framework that aims to make rational driving decisions by comprehensively modeling the interactions among agents. EGARL comprises three core components: a graphical representation of the traffic scenario that covers both topological and interactive information; an Edge-enhanced Graph Attention Network (E-GAT) that utilizes the graphical representation to extract interactive features by comprehensively considering nodes and edges of the graph; and a deep reinforcement learning method that generates driving decisions based on the current state and features extracted from E-GAT. Experimental results demonstrate the satisfying performance of EGARL. Our proposed framework can contribute to the development of intelligent transportation systems, enhancing the safety and efficiency of driving.