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Autonomous Intersection Management: Multi-Intersection Optimization.
Matthew
Hausknecht, Tsz-Chiu Au, and Peter
Stone.
In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September
2011.
[PDF]252.7kB [postscript]1.9MB
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.
@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. },
}
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