A Computationally Efficient Bi-Level Coordination Framework for CAVs at Unsignalized Intersections
Jiping Luo, Tingting Zhang, Qinyu Zhang
Abstract
In this article, 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: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">centralized</i> priority scheduling and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed</i> 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.