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As an emerging technology, Connected Autonomous Vehicles (CAVs) are believedto have the ability to move through intersections in a faster and safer manner,through effective Vehicle-to-Everything (V2X) communication and globalobservation. Autonomous intersection management is a key path to efficientcrossing at intersections, which reduces unnecessary slowdowns and stopsthrough adaptive decision process of each CAV, enabling fuller utilization ofthe intersection space. Distributed reinforcement learning (DRL) offers aflexible, end-to-end model for AIM, adapting for many intersection scenarios.While DRL is prone to collisions as the actions of multiple sides in thecomplicated interactions are sampled from a generic policy, restricting theapplication of DRL in realistic scenario. To address this, we propose ahierarchical RL framework where models at different levels vary in receptivescope, action step length, and feedback period of reward. The upper layer modelaccelerate CAVs to prevent them from being clashed, while the lower layer modeladjust the trends from upper layer model to avoid the change of mobile statecausing new conflicts. And the real action of CAV at each step is co-determinedby the trends from both levels, forming a real-time balance in the adversarialprocess. The proposed model is proven effective in the experiment undertaken ina complicated intersection with 4 branches and 4 lanes each branch, and showbetter performance compared with baselines.

HARL: A Novel Hierachical Adversary Reinforcement Learning for Automoumous Intersection Management