  Collaborative decision-making is an essential capability for multi-robot
systems, such as connected vehicles, to collaboratively control autonomous
vehicles in accident-prone scenarios. Under limited communication bandwidth,
capturing comprehensive situational awareness by integrating connected agents'
observation is very challenging. In this paper, we propose a novel
collaborative decision-making method that efficiently and effectively
integrates collaborators' representations to control the ego vehicle in
accident-prone scenarios. Our approach formulates collaborative decision-making
as a classification problem. We first represent sequences of raw observations
as spatiotemporal graphs, which significantly reduce the package size to share
among connected vehicles. Then we design a novel spatiotemporal graph neural
network based on heterogeneous graph learning, which analyzes spatial and
temporal connections of objects in a unified way for collaborative
decision-making. We evaluate our approach using a high-fidelity simulator that
considers realistic traffic, communication bandwidth, and vehicle sensing among
connected autonomous vehicles. The experimental results show that our
representation achieves over 100x reduction in the shared data size that meets
the requirements of communication bandwidth for connected autonomous driving.
In addition, our approach achieves over 30% improvements in driving safety.
