untitled
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<identifier>oai:HAL:hal-00850136v1</identifier>
<datestamp>2017-12-21</datestamp>
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<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Real time drunkenness analysis in a realistic car simulation</title>
<creator>Robinel, Audrey</creator>
<creator>Puzenat, Didier</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>The 20 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings - Bruges, Belgium from 25 to 27 April 2012 .</source>
<source>European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</source>
<coverage>Bruges, Belgium</coverage>
<contributor>ESANN</contributor>
<identifier>hal-00850136</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00850136</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00850136</source>
<source>ESANN. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2012, Bruges, Belgium. pp.85-90, 2012</source>
<language>en</language>
<subject lang=en>Artificial Neural Networks</subject>
<subject lang=en>Machine learning</subject>
<subject lang=en>behavioural analysis</subject>
<subject lang=en>driving</subject>
<subject lang=en>car</subject>
<subject lang=en>simulator</subject>
<subject lang=en>instrumentation</subject>
<subject lang=en>soberty</subject>
<subject lang=en>drunkenness</subject>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>This paper describes a blood alcohol content estimation method for car driver, based on a comportment analysis performed within a realistic simulation. An artificial neural network learns how to estimate subject's blood alcohol content. Low-level recording of user actions on the steering wheel and pedals are used to feed a multilayer perceptron, and a breathalyzer is used to build the learning examples set (desired output). Results are compared with a successful previous work based on a simple video game and demonstrate the ''complexity scalability'' of the approach.</description>
<date>2012-04-25</date>
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