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Kurt Dresner and Peter Stone. A Multiagent Approach to Autonomous Intersection Management. Journal of Artificial Intelligence Research, 31:591–656, March 2008.
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Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging. We believe current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. In this article, we suggest an alternative mechanism for coordinating the movement of autonomous vehicles through intersections. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol, which we also present. We demonstrate in simulation that our new mechanism has the potential to significantly outperform current intersection control technology---traffic lights and stop signs. Because our mechanism can emulate a traffic light or stop sign, it subsumes the most popular current methods of intersection control. This article also presents two extensions to the mechanism. The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles. The second gives priority to emergency vehicles without significant cost to civilian vehicles. The mechanism, including both extensions, is implemented and tested in simulation, and we present experimental results that strongly attest to the efficacy of this approach.
@Article{2008jair-dresner,
author="Kurt Dresner and Peter Stone",
title="A Multiagent Approach to Autonomous Intersection Management",
journal = {Journal of Artificial Intelligence Research},
month = {March},
year = {2008},
volume = {31},
pages = {591--656},
abstract = {
Artificial intelligence research is ushering in a new era of
sophisticated, mass-market transportation technology. While computers
can already fly a passenger jet better than a trained human pilot,
people are still faced with the dangerous yet tedious task of driving
automobiles. Intelligent Transportation Systems (ITS) is the field that
focuses on integrating information technology with vehicles and
transportation infrastructure to make transportation safer, cheaper, and
more efficient. Recent advances in ITS point to a
future in which vehicles themselves handle the vast majority of the
driving task. Once autonomous vehicles become popular, autonomous
interactions amongst \emph{multiple} vehicles will be possible. Current
methods of vehicle coordination, which are all designed to work with
human drivers, will be outdated. The bottleneck for roadway
efficiency will no longer be the drivers, but rather the mechanism by
which those drivers' actions are coordinated. While open-road driving
is a well-studied and more-or-less-solved problem, urban traffic
scenarios, especially intersections, are much more challenging.
We believe current methods for controlling traffic, specifically at
intersections, will not be able to take advantage of the increased
sensitivity and precision of autonomous vehicles as compared to human
drivers. In this article, we suggest an alternative mechanism for
coordinating the movement of autonomous vehicles through intersections.
Drivers and intersections in this mechanism are treated as autonomous agents
in a multiagent system. In this multiagent system, intersections use a new
reservation-based approach built around a detailed communication protocol,
which we also present. We demonstrate in simulation that our new mechanism
has the potential to significantly outperform current intersection control
technology---traffic lights and stop signs. Because our mechanism can
emulate a traffic light or stop sign, it subsumes the most popular current
methods of intersection control. This article also presents two extensions
to the mechanism. The first extension allows the system to control
human-driven vehicles in addition to autonomous vehicles. The second gives
priority to emergency vehicles without significant cost to civilian
vehicles. The mechanism, including both extensions, is implemented and
tested in simulation, and we present experimental results that strongly
attest to the efficacy of this approach.
},
bib2html_rescat = {Autonomous Intersection Management},
bib2html_pubtype = {Journal},
bib2html_funding = {NSF},
}
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