Socially-Optimal Auction-Theoretic Intersection Management System

Unsignalized intersections are often sources of congestion and collisions. When human-driven vehicles arrive simultaneously, the drivers typically creep out into the intersection or wave each other through to break stalemates. While intuitive for human drivers, this approach would be challenging for autonomous vehicles (AVs). Current AVs typically operate in isolation without explicitly communicating their intentions to others. In this paper, we propose an auction-based intersection management system (IMS) to determine a crossing schedule. Vehicles bid for crossing time using a cost function over different possible crossing times, and the IMS assigns crossing times that maximize social utility. We evaluate our system with an ambiguous crossing scenario and demonstrate its usefulness in determining socially-optimal crossing schedules.