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.
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Tango, F., Botta, M. (2009). Evaluation of Distraction in a Driver-Vehicle-Environment Framework: An Application of Different Data-Mining Techniques. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_15
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DOI: https://doi.org/10.1007/978-3-642-03067-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03066-6
Online ISBN: 978-3-642-03067-3
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