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Evaluation of Driver's Cognitive Distracted State Considering the Ambient State of a Car

Evaluation of Driver's Cognitive Distracted State Considering the Ambient State of a Car
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Author(s): Hiroaki Koma (Tokyo University of Science, Chiba, Japan), Taku Harada (Tokyo University of Science, Chiba, Japan), Akira Yoshizawa (Denso IT Laboratory, Inc., Tokyo, Japan) and Hirotoshi Iwasaki (Denso IT Laboratory, Inc., Tokyo, Japan)
Copyright: 2019
Volume: 13
Issue: 1
Pages: 12
Source title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)
Editor(s)-in-Chief: Kangshun Li (South China Agricultural University, China)
DOI: 10.4018/IJCINI.2019010102

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Abstract

The effectiveness of considering the ambient state of a driving car for evaluating the driver's cognitive distracted state is evaluated. In this article, Support Vector Machines and Random Forest, which are representative machine learning models, are applied. As input data for the machine learning model, in addition to a driver's biometric data and car driving data, an ambient state data of a driving car are used. The ambient state data of a driving car considered in this study are that of the preceding car and the shape of the road. Experiments using a driving simulator are conducted to evaluate the effectiveness of considering the ambient state of a driving car.

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