  Connected and automated vehicles (CAVs) have attracted more and more
attention recently. The fast actuation time allows them having the potential to
promote the efficiency and safety of the whole transportation system. Due to
technical challenges, there will be a proportion of vehicles that can be
equipped with automation while other vehicles are without automation. Instead
of learning a reliable behavior for ego automated vehicle, we focus on how to
improve the outcomes of the total transportation system by allowing each
automated vehicle to learn cooperation with each other and regulate
human-driven traffic flow. One of state of the art method is using
reinforcement learning to learn intelligent decision making policy. However,
direct reinforcement learning framework cannot improve the performance of the
whole system. In this article, we demonstrate that considering the problem in
multi-agent setting with shared policy can help achieve better system
performance than non-shared policy in single-agent setting. Furthermore, we
find that utilization of attention mechanism on interaction features can
capture the interplay between each agent in order to boost cooperation. To the
best of our knowledge, while previous automated driving studies mainly focus on
enhancing individual's driving performance, this work serves as a starting
point for research on system-level multi-agent cooperation performance using
graph information sharing. We conduct extensive experiments in car-following
and unsignalized intersection settings. The results demonstrate that CAVs
controlled by our method can achieve the best performance against several state
of the art baselines.
