An Efficient Message Dissemination Scheme for Cooperative Drivings via Cooperative Hierarchical Attention Reinforcement Learning

A group of connected and autonomous vehicles with common interests can drive in a cooperative manner, namely cooperative driving. In such a networked control system, an efficient message dissemination scheme is critical for cooperative drivings to periodically broadcast their kinetic status, i.e., beacon. However, most existing researches are designed for a simple or specific scenario, e.g., ignoring the impacts of the complex communication environment and emerging hybrid traffic scenarios. Worse still, the inevitable message transmission interference and the limited interaction among vehicles in harsh communication environments seriously hinder cooperation among cooperative drivings and deteriorate the beaconing performance. In this paper, we formulate the decision-making process of cooperative drivings as a Markov game. Furthermore, we propose a cooperative hierarchical attention reinforcement learning (CHA) framework to solve this Markov game. Specifically, the hierarchical structure of CHA leads cooperative drivings to be foresighted. Besides, we integrate each hierarchical level of CHA separately with graph attention networks to incorporate agents' mutual influences in the decision-making process. Moreover, each hierarchical level learns a cooperative reward function to motivate each agent to cooperate with others under harsh communication conditions. Finally, we set up a simulator and conduct extensive experiments to validate the effectiveness of CHA.