A new approach to detecting distracted car drivers using eye-movement data

Fumio Mizoguchi; Hiroyuki Nishiyama; Hirotoshi Iwasaki

doi:10.1109/ICCI-CC.2014.6921470

A new approach to detecting distracted car drivers using eye-movement data

Fumio Mizoguchi, Hiroyuki Nishiyama, Hirotoshi Iwasaki

Department of Industrial and Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

In our study, we generate new rules for determining whether or not a driver is distracted, using collected data about the driver's eye movement and driving data by learning as a new approach to detecting distracted car drivers. We use a learning tool, namely a support vector machine (SVM), to generate the rules. In addition, we focused on a qualitative model of a driver's cognitive mental load in a prior study and investigated the relationship between this model and the driver's distraction. In the investigation, we verify driver's eye movements and driving data that are inconsistent with the model.

Original language	English
Title of host publication	Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014
Editors	Shushma Patel, Yingxu Wang, Witold Kinsner, Dilip Patel, Gabriele Fariello, Lotfi A. Zadeh
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	266-272
Number of pages	7
ISBN (Electronic)	9781479960811
DOIs	https://doi.org/10.1109/ICCI-CC.2014.6921470
Publication status	Published - 10 Oct 2014
Event	13th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014 - London, United Kingdom Duration: 18 Aug 2014 → 20 Aug 2014

Publication series

Name	Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014

Conference

Conference	13th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014
Country/Territory	United Kingdom
City	London
Period	18/08/14 → 20/08/14

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10.1109/ICCI-CC.2014.6921470

Cite this

Mizoguchi, F., Nishiyama, H., & Iwasaki, H. (2014). A new approach to detecting distracted car drivers using eye-movement data. In S. Patel, Y. Wang, W. Kinsner, D. Patel, G. Fariello, & L. A. Zadeh (Eds.), Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014 (pp. 266-272). Article 6921470 (Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCI-CC.2014.6921470

Mizoguchi, Fumio ; Nishiyama, Hiroyuki ; Iwasaki, Hirotoshi. / A new approach to detecting distracted car drivers using eye-movement data. Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014. editor / Shushma Patel ; Yingxu Wang ; Witold Kinsner ; Dilip Patel ; Gabriele Fariello ; Lotfi A. Zadeh. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 266-272 (Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014).

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title = "A new approach to detecting distracted car drivers using eye-movement data",

abstract = "In our study, we generate new rules for determining whether or not a driver is distracted, using collected data about the driver's eye movement and driving data by learning as a new approach to detecting distracted car drivers. We use a learning tool, namely a support vector machine (SVM), to generate the rules. In addition, we focused on a qualitative model of a driver's cognitive mental load in a prior study and investigated the relationship between this model and the driver's distraction. In the investigation, we verify driver's eye movements and driving data that are inconsistent with the model.",

author = "Fumio Mizoguchi and Hiroyuki Nishiyama and Hirotoshi Iwasaki",

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doi = "10.1109/ICCI-CC.2014.6921470",

language = "English",

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Mizoguchi, F, Nishiyama, H & Iwasaki, H 2014, A new approach to detecting distracted car drivers using eye-movement data. in S Patel, Y Wang, W Kinsner, D Patel, G Fariello & LA Zadeh (eds), Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014., 6921470, Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 266-272, 13th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014, London, United Kingdom, 18/08/14. https://doi.org/10.1109/ICCI-CC.2014.6921470

A new approach to detecting distracted car drivers using eye-movement data. / Mizoguchi, Fumio; Nishiyama, Hiroyuki; Iwasaki, Hirotoshi.
Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014. ed. / Shushma Patel; Yingxu Wang; Witold Kinsner; Dilip Patel; Gabriele Fariello; Lotfi A. Zadeh. Institute of Electrical and Electronics Engineers Inc., 2014. p. 266-272 6921470 (Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - In our study, we generate new rules for determining whether or not a driver is distracted, using collected data about the driver's eye movement and driving data by learning as a new approach to detecting distracted car drivers. We use a learning tool, namely a support vector machine (SVM), to generate the rules. In addition, we focused on a qualitative model of a driver's cognitive mental load in a prior study and investigated the relationship between this model and the driver's distraction. In the investigation, we verify driver's eye movements and driving data that are inconsistent with the model.

AB - In our study, we generate new rules for determining whether or not a driver is distracted, using collected data about the driver's eye movement and driving data by learning as a new approach to detecting distracted car drivers. We use a learning tool, namely a support vector machine (SVM), to generate the rules. In addition, we focused on a qualitative model of a driver's cognitive mental load in a prior study and investigated the relationship between this model and the driver's distraction. In the investigation, we verify driver's eye movements and driving data that are inconsistent with the model.

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Mizoguchi F, Nishiyama H, Iwasaki H. A new approach to detecting distracted car drivers using eye-movement data. In Patel S, Wang Y, Kinsner W, Patel D, Fariello G, Zadeh LA, editors, Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 266-272. 6921470. (Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014). doi: 10.1109/ICCI-CC.2014.6921470

A new approach to detecting distracted car drivers using eye-movement data

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