Distraction during driving task is one of the most serious problems affecting traffic safety, being one of the main causes of accidents. Therefore, a method to diagnose and evaluate Distraction appears to be of paramount importance to study and implement efficient counter-measures. This research aims at illustrating our approach in diagnosis of Distraction status, comparing some of the widely used data-mining techniques; in particular, Fuzzy Logic (with Adaptive-Network-based Fuzzy Inference System) and Artificial Neural Networks. The results are compared to select which method gives the best performances.

Evaluation of Distraction in a Driver-Vehicle-Environment Framework: an application of different Data-mining techniques

BOTTA, Marco
2009-01-01

Abstract

Distraction during driving task is one of the most serious problems affecting traffic safety, being one of the main causes of accidents. Therefore, a method to diagnose and evaluate Distraction appears to be of paramount importance to study and implement efficient counter-measures. This research aims at illustrating our approach in diagnosis of Distraction status, comparing some of the widely used data-mining techniques; in particular, Fuzzy Logic (with Adaptive-Network-based Fuzzy Inference System) and Artificial Neural Networks. The results are compared to select which method gives the best performances.
9th Industrial Conference on Data Mining - ICDM09
Leipzig (Germany)
July 20-22, 2009
5633
176
190
machine learning
Fabio Tango; Marco Botta
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/70443
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