Evaluation of Distraction in a Driver-Vehicle-Environment Framework: An Application of Different Data-Mining Techniques
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.
KeywordsFuzzy Logic Adaptive-Network-based Fuzzy Inference System Neural Networks Machine Learning Distraction Traffic Safety
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