@article{ref1,
title="Construction of injury prediction models for vehicle occupants based on video - recorded drive recorder information",
journal="Transactions of Society of Automotive Engineers of Japan",
year="2024",
author="Yabugami, Kaede and Miyazaki, Yusuke and Kitamura, Koji and Sato, Fusako",
volume="55",
number="2",
pages="354-360",
abstract="To predict vehicle occupant injuries based on accident information obtained from a video-recorded drive recorder, injury prediction models were constructed by machine learning using information currently available from drive recorders and information that is expected to be acquired hereafter. Light Gradient Boosting Machine and Bayesian Networks were used for the machine learning models. The results showed that it was difficult to predict injuries with only the information currently available from a video-recorded drive recorder, while the prediction model with additional information expected to be available hereafter improved the prediction accuracy.   ===  ドライブレコーダーの情報から自動車乗員の傷害を予測することを目指し，ドライブレコーダーで現在取得可能な情報と，将来取得が見込まれる情報を用い，機械学習により傷害予測モデルを構築した．その結果，現在取得可能な情報のみでは予測困難だが，今後取得可能な情報が増すことで，予測精度を確保出来ることが分かった．<p /> <p>Language: ja</p>",
language="ja",
issn="0287-8321",
doi="10.11351/jsaeronbun.55.354",
url="http://dx.doi.org/10.11351/jsaeronbun.55.354"
}