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.

翻译：协作决策是多机器人系统（如网联车辆）在事故易发场景中协同控制自动驾驶车辆的关键能力。在有限通信带宽约束下，通过整合网联智能体的观测信息来获取全面态势感知极具挑战性。本文提出一种新颖的协作决策方法，该方法能够高效且有效地融合协作智能体的表征信息，用于控制事故易发场景中的自车。我们将协作决策形式化为分类问题。首先，将原始观测序列表示为时空图，这显著降低了网联车辆间共享数据包的大小。随后，基于异构图学习设计了一种新型时空图神经网络，该网络以统一方式分析目标的时空关联以实现协作决策。我们采用高保真度仿真器进行评估，该仿真器考虑了网联自动驾驶车辆间真实的交通流、通信带宽及感知特性。实验结果表明，本方法实现了超过100倍的数据共享压缩率，满足网联自动驾驶的通信带宽需求。此外，本方法在驾驶安全性方面提升了30%以上。