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@InProceedings{IROS11-hausknecht,
	author    = "Matthew Hausknecht and Tsz-Chiu Au and Peter Stone",
	title     = "Autonomous Intersection Management: Multi-Intersection Optimization",
	booktitle = "Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
	location  = "San Francisco, USA",
	month     = "September",
	year      = "2011",
	abstract  = { 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.  },
}