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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(3): 210-215 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.414

Classifying Cognitive Load and Driving Situation with Machine Learning

Yutaka Yoshida, Hayato Ohwada, Fumio Mizoguchi, and Hirotoshi Iwasaki
Abstract—This paper classifies a driver’s cognitive state in real driving situations to improve the in-vehicle information service that judges a user’s cognitive load and driving situation. We measure the driver’s eye movement and collect driving sensor data such as braking, acceleration, and steering angles that are used to classify the driver’s state. A set of data about the driver’s degree of cognitive load, regarded as a training set, is obtained from steering operation and task cognition. Given such information, we use a machine-learning method to classify the driver’s cognitive load. We achieved reasonable accuracy in certain driving situations in which the driver moves abnormally for an appropriate service supporting safe driving.

Index Terms—Driver’s cognitive load, eye movement, machine learning, driving task.

Yutaka Yoshida, Hayato Ohwada, and Fumio Mizoguchi are with the Tokyo University of Science, Noda-shi, Chiba-ken, Japan (e-mail: y-yoshida@ohwada-lab.net, ohwada@rs.tus.ac.jp, mizo@wisdomtex.com).
Hirotoshi Iwasaki is with Denso IT Laboratory, Inc., Japan (e-mail:hiwasaki@d-itlab.co.jp).

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Cite: Yutaka Yoshida, Hayato Ohwada, Fumio Mizoguchi, and Hirotoshi Iwasaki, "Classifying Cognitive Load and Driving Situation with Machine Learning," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 210-215, 2014.

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