  In this paper, we investigate cooperative vehicle coordination for connected
and automated vehicles (CAVs) at unsignalized intersections. To support high
traffic throughput while reducing computational complexity, we present a novel
collision region model and decompose the optimal coordination problem into two
sub-problems: \textit{centralized} priority scheduling and \textit{distributed}
trajectory planning. Then, we propose a bi-level coordination framework which
includes: (i) a Monte Carlo Tree Search (MCTS)-based high-level priority
scheduler aims to find high-quality passing orders to maximize traffic
throughput, and (ii) a priority-based low-level trajectory planner that
generates optimal collision-free control inputs. Simulation results demonstrate
that our bi-level strategy achieves near-optimal coordination performance,
comparable to state-of-the-art centralized strategies, and significantly
outperform the traffic signal control systems in terms of traffic throughput.
Moreover, our approach exhibits good scalability, with computational complexity
scaling linearly with the number of vehicles. Video demonstrations can be found
online at \url{https://youtu.be/WYAKFMNnQfs}.
