Ergonomics and safety of intelligent driver interfaces
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Non-intrusive Detection of Driver Distraction using Machine Learning Algorithms
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Driving distraction analysis by ECG signals: an entropy analysis
IDGD'11 Proceedings of the 4th international conference on Internationalization, design and global development
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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.