Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we describe a blood alcohol content estimation prototype based on a comportment analysis performed by artificial neural networks. We asked to subjects that had drunk alcohol to play a video-game after having measured their blood alcohol content with a breathalyser. A racing game was modified so that it could provide various data related to the use of the controls by the player. Using the collected data, we trained our neural network in order to be able to determine whether or not the subject had exceeded a blood alcohol content threshold. We also succeeded in estimating this blood alcohol content with a mean error of 0.1g/l. We could perform those estimations independently of the track played among the two ones used. It was also performed in "real time", e.g., using only the data collected within the last 10 seconds of playing.
Proceedings of the 2011 International Conference on Advances in Computer-Human Interactions
Gosier, Guadeloupe

Droits : info:eu-repo/semantics/OpenAccess
hal-00849971
https://hal.archives-ouvertes.fr/hal-00849971
https://hal.archives-ouvertes.fr/hal-00849971/document
https://hal.archives-ouvertes.fr/hal-00849971/file/article.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
We instrumented a car simulator by gathering low level data and fed it to an artificial neural network in order to perform blood alcohol content (BAC) estimations. The results depend on the quality of the data extraction and processing, and also on the selected inputs. We explain our data extraction and processing methodology, and how we used it to generate reliable and comparable features. At last, we describe the performances of individual features and how they combine. In the end, the prototype was able to accurately estimate the BAC value of a subject after being trained with driving samples of this subject with various BAC values.
Proceedings of ACHI 2013, The Sixth International Conference on Advances in Computer-Human Interactions
Nice, France

hal-00850137
https://hal.archives-ouvertes.fr/hal-00850137

Éditeur(s) : HAL CCSD
Résumé : International audience
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.
The 20 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings - Bruges, Belgium from 25 to 27 April 2012 .
Bruges, Belgium

hal-00850136
https://hal.archives-ouvertes.fr/hal-00850136

Éditeur(s) : HAL CCSD
Résumé : International audience
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.
ESANN 2013 proccedings,European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Bruges, Belgium

hal-00850138
https://hal.archives-ouvertes.fr/hal-00850138