Fabio Tango , Marco Botta " Real - time Detection System of Driver ’ s

There is an accumulating evidence that driver’s distraction is a leading cause of vehicle crashes and incidents. In particular, it has become an important and growing safety concern with the increasing use of the so-called In-Vehicle Information Systems (IVIS) and Partially Autonomous Driving Assistance Systems (PADAS). Thereby, the detection of the driver status is of paramount importance, in order to adapt IVIS and PADAS accordingly, so avoiding or mitigating their possible negative effects. The purpose of this paper is to illustrate a method for the non-intrusive and real-time detection of visual distraction, based on vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models, based on well-known Machine Learning methods. 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 feature selection criteria have been compared. Based on our results, SVM has outperformed all the other ML methods, providing the highest classification rate for most of the subjects. Potential applications of this research include the design of adaptive IVIS and of “smarter” PADAS. Index Terms — Accident prevention; artificial intelligence and machine learning; driver’ distraction and inattention; intelligent supporting systems.