Integration of Microsimulation and Optimized Autonomous Intersection Management

Abstract

Autonomous intersection management (AIM) is a type of intersection control for autonomous vehicles which eliminates the need for a traffic signal by using vehicle-to-infrastructure communication. Vehicles communicate information to an intersection manager which determines vehicle ordering and spacing so that vehicles can pass safely through the intersection. Reservation-based AIM, which gives vehicles space-time path reservations through an intersection, has the potential to greatly increase the capacity of intersections by allowing an intersection controller to optimize the path that each vehicle takes. A mixed-integer linear program is proposed which gives the intersection manager more flexibility through optimizing vehicle acceleration and velocity through the intersection. This model was integrated with microsimulation software and various scenarios were simulated, including fluctuating the vehicle demands, altering the permitted vehicle accelerations and speeds, and modifying the safety buffer between vehicles. The results indicate that the model proposed in this study has the capability to reduce delay and increase average speed experienced by vehicles compared with the existing reservation-based intersection control formulations and conventional signal controls.