Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections

Road intersection is one of the most complex and accident-prone traffic scenarios, so it’s challenging for autonomous vehicles (AVs) to make safe and efficient decisions at the intersections. Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency. To address these problems, this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically, safely and efficiently. The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks. Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles. Markov decision process was employed to model the interaction between AVs and other vehicles, and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency. To verify the effectiveness of the proposed decision-making framework, the top three accident-prone crossing path crash scenarios at intersections were simulated, when different initial vehicle states were adopted for better generalization capability. The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.