Learning to Drive at Unsignalized Intersections using Attention-based Deep Reinforcement Learning

Abstract

Driving at an unsignalized intersection is a complex traffic scenario that requires both traffic safety and efficiency. At the unsignalized intersection, the driving policy does not simply maintain a safe distance for all vehicles. Instead, it pays more attention to vehicles that potentially have conflicts with the ego vehicle, while guessing the intentions of other vehicles. In this paper, we propose an attention-based driving policy for handling unprotected intersections using deep reinforcement learning. By leveraging attention, our policy learns to focus on more spatially and temporally important features within its egocentric observation. This selective attention enables our policy to make safe and efficient driving decisions in various congested intersection environments. Our experiments show that the proposed policy not only outperforms other baseline policies in various intersection scenarios and traffic density conditions but also has interpretability in its decision process. Furthermore, we verify our policy model's feasibility in real-world deployment by transferring the trained model to a full-scale vehicle system. Our model successfully performs various intersection scenarios, even with noisy sensory data and delayed responses. Our approach reveals more opportunities for implementing generic and interpretable policy models in real-world autonomous driving.