Analyzing Behavior and Intention in Multi-Agent Systems Using Graph Neural Networks

Jintai Du, Jinlong Wang, Jiansheng Chen, Xinlong Ding, Jiehui Wu, Tianyu Hu, Huimin Ma

Abstract: Multi-agent behavior and intention analysis has been applied in many aspects of our daily lives such as driving maneuver anticipation and assistive driving perception. However, the research in this field suffers from the lack of publicly available datasets, which is mainly caused by the low availability and high complexity of the multi-agent behavior and intention data. In this paper, we propose MBI, a dataset that contains five categories of agents in eight scenarios based on an open-source simulated platform called HarFang3D DogFight SandBox. In MBI, five types of behaviors and five types of intentions are collected. Additionally, we benchmark different Graph Neural Networks (GNNs) on the proposed dataset to test their performance on the tasks of analyzing different behaviors and intentions in multi-agent systems. We also propose a new method called D 2 RGAT and find it can achieve the best results on MBI.