Autonomous Intersection Management: Multi-Intersection Optimization (2011)
Matthew Hausknecht, Tsz-Chiu Au, and Peter Stone
Advances in autonomous vehicles and Intelligent Transportation Systems indicate a rapidly approaching future in which intelligent vehicles will automatically handle the process of driving. However, increasing the efficiency of today’s transportation infrastructure will require intelligent traffic control mechanisms that work hand in hand with intelligent vehicles. To this end, Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that by studying the problem from a multi-agent perspective, intersection control can be made more efficient than existing control mechanisms such as traffic signals and stop signs. We extend their study beyond the case of an individual intersection and examine the unique implications and abilities afforded by using AIM-based agents to control a network of interconnected intersections. We examine different navigation policies by which autonomous vehicles can dynamically alter their planned paths, observe an instance of Braess’ paradox, and explore the new possibility of dynamically reversing the flow of traffic along lanes in response to minute-by-minute traffic conditions. Studying this multi-agent system in simulation, we quantify the substantial improvements in efficiency imparted by these agent-based traffic control methods.
In Proceedings of IROS 2011-IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), September 2011.

Tsz-Chiu Au Postdoctoral Alumni chiu [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu