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Learning Policy Selection for Autonomous Intersection Management.
Kurt
Dresner and Peter Stone.
In AAMAS 2007 Workshop on Adaptive and Learning
Agents, pp. 34–39, Honolulu, Hawaii, USA, May 2007.
[PDF]79.9kB [postscript]197.6kB
Few aspects of modern life inflict as high a cost on society as traffic congestion and automobile accidents. Current work in AI and Intelligent Transportation Systems aims to replace human drivers with autonomous vehicles capable of safely and efficiently navigating through the most hazardous city streets. Once such vehicles are common, interactions between multiple vehicles will be possible. Traffic lights and stop signs, which were designed for human drivers, may no longer be the best method for intersection control. Previously, we made the case for a reservation-based intersection control mechanism designed for autonomous vehicles, but compatible with human drivers. Including human drivers allows incremental deployability as well as support for those who drive for pleasure, but may result in significantly suboptimal performance, as human drivers may be present in dramatically varying proportions. In this paper, we develop a learning-based approach to determine which variant of the control mechanism will be most effective under given conditions, and then combine the resulting predictor with our multiagent intersection management mechanism, enabling it to determine when and how it should alter its configuration to best suit the current traffic conditions. Our extension is fully implemented and tested in simulation, and we provide experimental results demonstrating its efficacy.
@InProceedings{2007alag-dresner,
author="Kurt Dresner and Peter Stone",
title="Learning Policy Selection for Autonomous Intersection Management",
booktitle="AAMAS 2007 Workshop on Adaptive and Learning Agents",
address="Honolulu, Hawaii, USA",
month="May", year="2007",
pages= "34--39",
abstract={Few aspects of modern life inflict as high a cost on
society as traffic congestion and automobile accidents.
Current work in AI and Intelligent Transportation Systems aims
to replace human drivers with autonomous vehicles capable of
safely and efficiently navigating through the most hazardous
city streets. Once such vehicles are common, interactions
between multiple vehicles will be possible. Traffic lights
and stop signs, which were designed for human drivers, may no
longer be the best method for intersection control.
Previously, we made the case for a reservation-based
intersection control mechanism designed for autonomous
vehicles, but compatible with human drivers. Including human
drivers allows incremental deployability as well as support
for those who drive for pleasure, but may result in
significantly suboptimal performance, as human drivers may be
present in dramatically varying proportions. In this paper, we
develop a learning-based approach to determine which variant
of the control mechanism will be most effective under given
conditions, and then combine the resulting predictor with our
multiagent intersection management mechanism, enabling it to
determine when and how it should alter its configuration to
best suit the current traffic conditions. Our extension is
fully implemented and tested in simulation, and we provide
experimental results demonstrating its efficacy. },
}
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