@inproceedings{deb7bc6cd5a54e4c81cbc1f74e742a1b,
title = "Machine-learning approach to analysis of driving simulation data",
abstract = "In our study, we sought to generate rules for cognitive distractions of car drivers using data from a driving simulation environment. We collected drivers' eye-movement and driving data from 18 research participants using a simulator. Each driver drove the same 15-minute course two times. The first drive was normal driving (no-load driving), and the second drive was driving with a mental arithmetic task (load driving), which we defined as cognitive-distraction driving. To generate rules of distraction driving using a machine-learning tool, we transformed the data at constant time intervals to generate qualitative data for learning. Finally, we generated rules using a Support Vector Machine (SVM).",
keywords = "Car Driving Simulation, Eye-Movement Data, Machine Learning, Support Vector Machine",
author = "Akira Yoshizawa and Hiroyuki Nishiyama and Hirotoshi Iwasaki and Fumio Mizoguchi",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 15th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016 ; Conference date: 22-08-2016 Through 23-08-2016",
year = "2017",
month = feb,
day = "21",
doi = "10.1109/ICCI-CC.2016.7862067",
language = "English",
series = "Proceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "398--402",
editor = "Kostas Plataniotis and Bernard Widrow and Newton Howard and Zadeh, \{Lotfi A.\} and Yingxu Wang",
booktitle = "Proceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016",
}