Intersection Crossing with Connected Autonomous Vehicles under Location Uncertainty

Abstract

Intersections are among the most challenging sections of our road infrastructure and a clear bottleneck for traffic flows. Key aspects of the 5G cellular network, e.g., the Multi Access Edge Computational (MEC) platform and the reduced network latency, act as enablers for the utilization of Connected Autonomous Vehicles (CAVs) that can ultimately bring about drastic changes in the management of intersection crossings and transportation networks in general. To date, there exist extensive research on the problem of autonomous intersection crossings facilitated by CAVs, but the majority of these works assumes that CAVs know their exact location and system state. This work presents a novel framework that allows for an optimized Intersection Manager (IM) that also considers vehicle location uncertainties. Building upon the proposed optimization framework, AVOID is presented, a real-time, near-optimal algorithm that maximizes intersection throughput with probabilistic collision avoidance guarantees. Extensive simulations assess the performance of AVOID, in terms of safe distance between vehicles and intersection throughput, and the effects of the update communication frequency (from the vehicles to the IM) on the gains of the proposed framework.