Since the traffic administration at road intersections determines the capacity bottleneck of modern transportation systems, intelligent cooperative coordination for connected autonomous vehicles (CAVs) has shown to be an effective solution. In this paper, we try to formulate a Bi-Level CAV intersection coordination framework, where coordinators from High and Low levels are tightly coupled. In the High-Level coordinator where vehicles from multiple roads are involved, we take various metrics including throughput, safety, fairness and comfort into consideration. Motivated by the time consuming space-time resource allocation framework in [1], we try to give a low complexity solution by transforming the complicated original problem into a sequential linear programming one. Based on the "feasible tunnels" (FT) generated from the High-Level coordinator, we then propose a rapid gradient-based trajectory optimization strategy in the Low-Level planner, to effectively avoid collisions beyond High-level considerations, such as the pedestrian or bicycles. Simulation results and laboratory experiments show that our proposed method outperforms existing strategies. Moreover, the most impressive advantage is that the proposed strategy can plan vehicle trajectory in milliseconds, which is promising in realworld deployments. A detailed description include the coordination framework and experiment demo could be found at the supplement materials, or online at https://youtu.be/MuhjhKfNIOg.

翻译：由于道路交叉口的交通管理决定了现代交通系统的容量瓶颈，针对网联自动驾驶汽车（CAVs）的智能协同协作已被证明是一种有效解决方案。本文尝试构建一种双层CAV交叉口协同框架，其中高层与低层协调器实现紧耦合。在涉及多道路车辆的高层协调器中，我们综合考虑了吞吐量、安全性、公平性和舒适性等多项指标。受文献[1]中耗时时空资源分配框架的启发，我们通过将复杂原始问题转化为序列线性规划问题，提出了一种低复杂度解决方案。基于高层协调器生成的"可行隧道"（FT），我们在低层规划器中进一步提出了一种基于梯度的快速轨迹优化策略，以有效规避高层考量之外的碰撞风险（如行人或自行车）。仿真结果与实验室实验表明，所提方法优于现有策略。最显著的优点是该方法能在毫秒级时间内完成车辆轨迹规划，极具实际部署潜力。关于协同框架与实验演示的详细说明可参见补充材料，或访问在线链接：https://youtu.be/MuhjhKfNIOg。