Continuous decision‐making for autonomous driving at intersections using deep deterministic policy gradient

Abstract Intersections have been identified as the most complex and accident‐prone traffic scenarios on road. Making appropriate decisions at intersections for driving safety, efficiency, and comfort become a challenging task for autonomous vehicles (AVs). The existing research on AV decision‐making at intersections either considers a single scenario only with discrete behaviour outputs or ignores the requirements for driving efficiency and comfort. To address these problems, this study proposed a deep reinforcement learning based continuous decision‐making method to make AVs drive through intersections. The proposed method establishes an end‐to‐end decision‐making framework by using a convolutional neural network to map the relationship between traffic images and vehicle operations. The interaction between the AV and other vehicles was modelled as a Markov decision process (MDP), and a deep deterministic policy gradient algorithm was employed to solve the MDP problem and obtain the optimal driving policy. The top three accident‐prone crossing path crash scenarios at intersections were realized in CARLA to verify the effectiveness of the proposed method. The experimental results demonstrated that the developed method could provide effective policies to ensure driving safety and efficiency while considering driving comfort for autonomous driving at intersections in all the examined scenarios.