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Experiments using a driving simulator are conducted to evaluate the effectiveness of considering the ambient state of a driving car.<\/p>","DOI":"10.4018\/ijcini.2019010102","type":"journal-article","created":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T11:42:50Z","timestamp":1551267770000},"page":"13-24","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of Driver's Cognitive Distracted State Considering the Ambient State of a Car"],"prefix":"10.4018","volume":"13","author":[{"given":"Hiroaki","family":"Koma","sequence":"first","affiliation":[{"name":"Tokyo University of Science, Chiba, Japan"}]},{"given":"Taku","family":"Harada","sequence":"additional","affiliation":[{"name":"Tokyo University of Science, Chiba, Japan"}]},{"given":"Akira","family":"Yoshizawa","sequence":"additional","affiliation":[{"name":"Denso IT Laboratory, Inc., Tokyo, Japan"}]},{"given":"Hirotoshi","family":"Iwasaki","sequence":"additional","affiliation":[{"name":"Denso IT Laboratory, Inc., Tokyo, Japan"}]}],"member":"2432","reference":[{"key":"IJCINI.2019010102-0","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"IJCINI.2019010102-1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"IJCINI.2019010102-2","doi-asserted-by":"publisher","DOI":"10.4018\/ijssci.2014010101"},{"key":"IJCINI.2019010102-3","first-page":"119","article-title":"Evaluation of Cognitive Distracted State based on Classified Eye Movement Types","author":"T.Harada","year":"2015","journal-title":"Proceedings of 30th International Conference on Computers and Their Applications"},{"key":"IJCINI.2019010102-4","doi-asserted-by":"publisher","DOI":"10.4018\/IJSSCI.2015070101"},{"key":"IJCINI.2019010102-5","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2014.6957813"},{"key":"IJCINI.2019010102-6","doi-asserted-by":"publisher","DOI":"10.4018\/IJCINI.2017010102"},{"key":"IJCINI.2019010102-7","doi-asserted-by":"crossref","unstructured":"Liu, T., & Yang, Y. Huang, Guang-Bin, & Lin, Z. (2015). Detection of Drivers' Distraction Using Semi-Supervised Extreme Learning Machine. In Proceedings of Adaptation, Learning and Optimization, The International Conference on Extreme Learning Machine (Vol. 2, pp. 379-387).","DOI":"10.1007\/978-3-319-14066-7_36"},{"key":"IJCINI.2019010102-8","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2015.217"},{"key":"IJCINI.2019010102-9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2496157"},{"key":"IJCINI.2019010102-10","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2010.5624966"},{"key":"IJCINI.2019010102-11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCI-CC.2014.6921470"},{"key":"IJCINI.2019010102-12","unstructured":"Mizoguchi, F., Ohwada, H., Nishiyama, H., Yoshizawa, A., & Iwasaki, H. (2015). Extracting rules to detect cognitive distractions through driving simulation. In Late Breaking Papers of the 25th International Conference on Inductive Logic Programming."},{"key":"IJCINI.2019010102-13","doi-asserted-by":"crossref","unstructured":"Sato, K., Katsumata, K., Ito, M., Madokoro, H. & Kadowaki, S. (2015). Driver Body Information Analysis for Distraction State Detection. In Forum on Information Technology (pp. 35-43). 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