Real time drunkenness analysis in a realistic car simulation

Abstract : 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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00850136
Contributor : Audrey Robinel <>
Submitted on : Saturday, August 3, 2013 - 10:17:39 PM
Last modification on : Wednesday, July 18, 2018 - 8:11:27 PM

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  • HAL Id : hal-00850136, version 1

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Audrey Robinel, Didier Puzenat. Real time drunkenness analysis in a realistic car simulation. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2012, Bruges, Belgium. pp.85-90. ⟨hal-00850136⟩

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