Neuroevolution of an Automobile Crash Warning System. Kenneth
Stanley, Nate Kohl, Rini Sherony, and Risto
Miikkulainen. In Proceedings of the Genetic and Evolutionary Computation Conference 2005, pp. 1977–1984,
2005.
http://www.isgec.org/gecco-2005/
Many serious automobile accidents could be avoided if drivers were warned of impending crashes before they occurred. In this paper, a vehicle warning system is evolved to predict such crashes in the RARS driving simulator. The NeuroEvolution of Augmenting Topologies (NEAT) method is first used to evolve a neural network driver that can autonomously navigate a track without crashing. The network is subsequently impaired, resulting in a driver that occasionally makes mistakes and crashes. Using this impaired driver, a crash predictor is evolved that can predict how far in the future a crash is going to occur, information that can be used to generate an appropriate warning level. The main result is that NEAT can successfully evolve a warning system that takes into account the recent history of inputs and outputs, and therefore makes few errors. Experiments were also run to compare training offline from previously collected data with training online in the simulator. While both methods result in successful warning systems, offline training is both faster and more accurate. Thus, the results in this paper set the stage for developing crash predictors that are both accurate and able to adapt online, which may someday save lives in real vehicles.
@InProceedings{stanley:gecco05,
author = "Kenneth Stanley and Nate Kohl and Rini Sherony and Risto Miikkulainen",
title = "Neuroevolution of an Automobile Crash Warning System",
booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference 2005",
year = "2005",
pages = "1977--1984",
abstract = {
Many serious automobile accidents could be avoided if drivers were
warned of impending crashes before they occurred. In this paper, a
vehicle warning system is evolved to predict such crashes in the
RARS driving simulator. The NeuroEvolution of Augmenting Topologies
(NEAT) method is first used to evolve a neural network driver that
can autonomously navigate a track without crashing. The network is
subsequently impaired, resulting in a driver that occasionally makes
mistakes and crashes. Using this impaired driver, a crash predictor
is evolved that can predict how far in the future a crash is going
to occur, information that can be used to generate an appropriate
warning level. The main result is that NEAT can successfully evolve
a warning system that takes into account the recent history of
inputs and outputs, and therefore makes few errors. Experiments
were also run to compare training offline from previously collected
data with training online in the simulator. While both methods
result in successful warning systems, offline training is both
faster and more accurate. Thus, the results in this paper set the
stage for developing crash predictors that are both accurate and
able to adapt online, which may someday save lives in real vehicles.
},
wwwnote = {<a href="http://www.isgec.org/gecco-2005/">http://www.isgec.org/gecco-2005/</a>},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Machine Learning}
}
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