The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Applications of Support Vector Machines for Pattern Recognition: A Survey
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Automation effects on driver's behaviour when integrating a PADAS and a distraction classifier
ICDHM'11 Proceedings of the Third international conference on Digital human modeling
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Driver's distraction has become an important and growing safety concern with the increasing use of the so-called In-Vehicle Information Systems (IVIS), such as cell-phones, navigation systems, etc. A very promising way to overcome this problem is to detect driver's distraction and thus to adopt in-vehicle systems accordingly, in order to avoid or mitigate the negative effects. The purpose of this paper is to illustrate a method for the non-intrusive detection of visual distraction, based on the vehicle dynamic data; in particular, we present and compare two models, applying Artificial Neural Networks (ANN) and Support Vector Machines (SVM) which are well-known data-mining methods. Despite of what already done in literature, our method does not use eye-tracker data in the final classifier. With respect to other similar works, we regard distraction identification as a classification problem and, moreover, we extend the datasets, both in terms of data-points and of scenarios. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task (SURT) while driving. Different training methods, model characteristics and features selection criteria have been compared. Potential applications of this research include the design of adaptive IVIS and of “smarter” Partially Autonomous Driving Assistance Systems (PADAS), as well as the evaluation of driver's distraction.