@article{ref1,
title="Extraction of feature quantities of vehicle behavior felt by drivers using machine learning and deep learning",
journal="Transactions of Society of Automotive Engineers of Japan",
year="2021",
author="Kobayashi, Hiroaki and Katori, Yoshiaki and Fujishiro, Sen and Yamashita, Taro and Tachioka, Koji and Miyashiro, Daisuke",
volume="52",
number="1",
pages="37-42",
abstract="To quantify the difference of ordinary drive steering response performance by measurement data is one of an issue that OEMs have faced difficulty for a long time. In particular, slight difference in vehicle behavior induced by body reinforcement is widely known by recent studies (1-4). In this study, we tried to identify vehicle behavior signals to quantify such effect as &quot;vehicle behavior felt by drivers&quot;. In order to extract the effect of &quot;vehicle behavior felt by drivers&quot; with and without body reinforcement objectively, we analyzed a large amount of measured time-series data using machine learning and deep learning.<p /> <p>Language: ja</p>",
language="ja",
issn="0287-8321",
doi="10.11351/jsaeronbun.52.37",
url="http://dx.doi.org/10.11351/jsaeronbun.52.37"
}