  Connected and automated vehicles (CAVs) are viewed as a special kind of
robots that have the potential to significantly improve the safety and
efficiency of traffic. In contrast to many swarm robotics studies that are
demonstrated in labs by employing a small number of robots, CAV studies aims to
achieve cooperative driving of unceasing robot swarm flows. However, how to get
the optimal passing order of such robot swarm flows even for a signal-free
intersection is an NP-hard problem (specifically, enumerating based algorithm
takes days to find the optimal solution to a 20-CAV scenario). Here, we
introduce a novel cooperative driving algorithm (AlphaOrder) that combines
offline deep learning and online tree searching to find a near-optimal passing
order in real-time. AlphaOrder builds a pointer network model from solved
scenarios and generates near-optimal passing orders instantaneously for new
scenarios. Furthermore, our approach provides a general approach to managing
preemptive resource sharing between swarm robotics (e.g., scheduling multiple
automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at
conflicting areas
