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

Hiroaki Koma, Taku Harada, Akira Yoshizawa and Hirotoshi Iwasaki
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Hiroaki Koma: Tokyo University of Science, Chiba, Japan
Taku Harada: Tokyo University of Science, Chiba, Japan
Akira Yoshizawa: Denso IT Laboratory, Inc., Tokyo, Japan
Hirotoshi Iwasaki: Denso IT Laboratory, Inc., Tokyo, Japan

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2019, vol. 13, issue 1, 13-24

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:13:y:2019:i:1:p:13-24

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