Evaluation of Distraction in a Driver-Vehicle-Environment Framework: An Application of Different Data-Mining Techniques

  • Fabio Tango
  • Marco Botta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5633)


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.


Fuzzy Logic Adaptive-Network-based Fuzzy Inference System Neural Networks Machine Learning Distraction Traffic Safety 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fabio Tango
    • 1
  • Marco Botta
    • 1
  1. 1.Department of Computer ScienceUniversity of TorinoItaly

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