Multi-User Blood Alcohol Content Estimation in a Realistic Simulator using Artificial Neural Networks and Support Vector Machines

Abstract : We instrumented a realistic car simulator to extract low level data related to the driver's use of the vehicle controls. After proceeding these data, we generated features that were fed to a Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Our goal was determine if the driver's Blood Alcohol Content (BAC) was over $0.4g.l^{-1}$ or not, and even estimate the BAC value. Our device process the vehicle's controls data and then outputs the user BAC. We discuss the results of the prototype using the MLP and SVM algorithms in both single-user and multi-user context for detection of drunk drivers and estimation of the BAC value. The prototype performed better with single user base than with multi-user, and provided comparable results with MLP and SVM.
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Communication dans un congrès
ESANN 2013, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2013, Bruges, Belgium. pp.431-436, 2013
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https://hal.archives-ouvertes.fr/hal-00850138
Contributeur : Audrey Robinel <>
Soumis le : samedi 3 août 2013 - 22:45:22
Dernière modification le : samedi 3 août 2013 - 22:45:22

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  • HAL Id : hal-00850138, version 1

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Audrey Robinel, Didier Puzenat. Multi-User Blood Alcohol Content Estimation in a Realistic Simulator using Artificial Neural Networks and Support Vector Machines. ESANN 2013, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2013, Bruges, Belgium. pp.431-436, 2013. <hal-00850138>

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