%0 Journal Article
%T Extraction of feature quantities of vehicle behavior felt by drivers using machine learning and deep learning
%J Transactions of Society of Automotive Engineers of Japan
%D 2021
%A Kobayashi, Hiroaki
%A Katori, Yoshiaki
%A Fujishiro, Sen
%A Yamashita, Taro
%A Tachioka, Koji
%A Miyashiro, Daisuke
%V 52
%N 1
%P 37-42
%X 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 "vehicle behavior felt by drivers". In order to extract the effect of "vehicle behavior felt by drivers" 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>
%G ja
%I Society of Automotive Engineers of Japan
%@ 0287-8321
%U http://dx.doi.org/10.11351/jsaeronbun.52.37