@article{ref1,
title="Driving feature extraction from high and low skilled drivers in curve sections based on machine learning",
journal="Journal of mechanical systems for transportation and logistics",
year="2013",
author="Li, Shuguang and Yamabe, Shigeyuki and Sato, Yoichi and Hirasawa, Takayuki and Suda, Yoshihiro and Chandrasiri, Naiwala P. and Nawa, Kazunari",
volume="6",
number="2",
pages="111-123",
abstract="We have developed a new driving assistance system that can help low-skilled drivers  improve their driving skills. We did this in three steps. First, we developed a  statistical method to extract distinctions between high- and low-skilled drivers on the  basis of AdaBoost, which selects a small number of critical operation features  between high- and low-skilled drivers. Second, we built a driving skill evaluation  model on the basis of the extracted features. Finally, we performed a series of  experiments using a driving simulator, in which advice based on extracted features  was supplied to low-skilled drivers and was expected to improve their driving skill. We also proposed an index for evaluating driving skill change, and results show the  advice effectively improved the drivers' driving skills.<p /> <p>Language: en</p>",
language="en",
issn="1882-1782",
doi="10.1299/jmtl.6.111",
url="http://dx.doi.org/10.1299/jmtl.6.111"
}