With the development of vehicle-to-infrastructure and vehicle-to-vehicle technologies, vehicles will be able to communicate with the controller at the intersection. Autonomous driving technology enables vehicles to follow the instructions sent from the controller precisely. Autonomous intersection management considers each vehicle as an agent and coordinates vehicle trajectories to resolve vehicle conflicts inside an intersection. This study proposes an autonomous intersection management algorithm called AIM-ped considering both vehicles and pedestrians which is able to produce the total optimal throughput when combined with max pressure control. This study analyzes the stability properties of the algorithm based on a simpler version of AIM-ped, which is a conflict region model of the autonomous intersection management. To implement the proposed algorithm in simulation, this study combines the max-pressure control with an existing trajectory optimization algorithm to calculate optimal vehicle trajectories. Simulations are conducted to test the effects of pedestrian demand on intersection efficiency. The simulation results show that delays of pedestrians and vehicles are negatively correlated and the proposed algorithm can adapt to the change in the pedestrian demand and activate vehicle movements with conflicting trajectories.

随着车辆到基础设施和车辆到车辆技术的发展，车辆将能够在十字路口与控制器通信。自动驾驶技术使车辆能够精确地遵循控制器发出的指令。自治交叉路口管理将每个车辆视为代理，并协调车辆轨迹以解决交叉路口内的车辆冲突。这项研究提出了一种称为AIM- ped的自动交叉路口管理算法，该算法同时考虑了车辆和行人，当与最大压力控制相结合时能够产生总的最佳吞吐量。这项研究基于AIM- ped的简单版本分析了算法的稳定性，这是自治路口管理的冲突区域模型。为了在仿真中实现所提出的算法，本研究将最大压力控制与现有的轨迹优化算法相结合，以计算出最佳的车辆轨迹。进行仿真以测试行人需求对交叉路口效率的影响。仿真结果表明，行人与车辆的时延呈负相关，所提算法能够适应行人需求的变化，并激活轨迹冲突的车辆运动。