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

TL;DR

This paper introduces a hierarchical reinforcement learning framework for autonomous intersection management, improving safety and efficiency by coordinating vehicle actions through multi-level decision models.

Contribution

It proposes a novel hierarchical RL approach with adversarial elements, enhancing collision avoidance and traffic flow in complex intersection scenarios.

Findings

Outperforms baseline methods in complex intersection tests

Reduces collision rates in simulated environments

Improves traffic throughput and safety metrics

Abstract

As an emerging technology, Connected Autonomous Vehicles (CAVs) are believed to have the ability to move through intersections in a faster and safer manner, through effective Vehicle-to-Everything (V2X) communication and global observation. Autonomous intersection management is a key path to efficient crossing at intersections, which reduces unnecessary slowdowns and stops through adaptive decision process of each CAV, enabling fuller utilization of the intersection space. Distributed reinforcement learning (DRL) offers a flexible, end-to-end model for AIM, adapting for many intersection scenarios. While DRL is prone to collisions as the actions of multiple sides in the complicated interactions are sampled from a generic policy, restricting the application of DRL in realistic scenario. To address this, we propose a hierarchical RL framework where models at different levels vary in receptive scope, action step length, and feedback period of reward. The upper layer model accelerate CAVs to prevent them from being clashed, while the lower layer model adjust the trends from upper layer model to avoid the change of mobile state causing new conflicts. And the real action of CAV at each step is co-determined by the trends from both levels, forming a real-time balance in the adversarial process. The proposed model is proven effective in the experiment undertaken in a complicated intersection with 4 branches and 4 lanes each branch, and show better performance compared with baselines.