FACULTY AND RESEARCH

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.