Injury Prediction for Emergency Rescue using Degital Human Models

使用数字人体模型进行紧急救援伤害预测

• 批准号: 23500513
• 负责人: UJIHASHI SADAYUKI
• 金额: $ 3.24万
• 依托单位: Nippon Bunri University (2012-2013)Tokyo Institute of Technology (2011)
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2011
• 资助国家: 日本
• 起止时间: 2011 至 2013
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23500513/
• 关键词: 交通事故; 自動車乗員; 傷害予測; 衝突事故; 人体デジタルモデル; 有限要素法; 自動車有限要素モデル

Injury prediction based on data from in-vehicle devices, known as AACN, is expected to reduce trauma deaths. The existing prediction method developed by statistical analysis of the accident database used delta-V as the factor.However, crash pattern such as wrap-ratio should be considered, as it also affects injury outcomes. Numerical simulations of various frontal crashes with vehicle and occupant dummy models were performed. The results were classified into seven categories based on waveform of vehicle acceleration by cluster analysis, and prediction equations were developed as function of delta-V and the category. It was revealed that delta-V and the category could represent the acceleration features, and compartment intrusion depended on the category. The equations were significantly different among the categories and predicted well dummy's injuries in tests. Thus, this study suggested a method to develop injury prediction equations with two factors for accurate and immediate AACN.

基于车载设备（AACN）数据的伤害预测预计将减少创伤死亡。现有的预测方法通过对事故数据库的统计分析来开发，使用delta-V作为因子。然而，应该考虑碰撞模式，如包裹率，因为它也会影响伤害结果。采用车辆和乘员假人模型对各种正面碰撞进行了数值模拟。根据车辆加速度波形，通过聚类分析将结果分为7类，并建立了预测方程，作为delta-V和类别的函数。结果表明，Δ V和类别可以代表加速度特征，并且分区侵入依赖于类别。各类别间的模型差异显著，对假人的损伤有较好的预测作用。因此，本研究提出了一种方法，以发展伤害预测方程的两个因素，准确和即时AACN。

项目成果

Estimation of Vehicle Deformation based on Acceleration from Event Data Recorder

基于事件数据记录器加速度的车辆变形估计

• 发表时间: 2012
• 期刊: Proceedings of the 6th International Conference on Coupled Instabilities in Metal Structures (CIMS)
• 作者: Katagiri;M.;Tsubouchi;H.;Ujihashi;S.;Fukaya;T.;Awano;M.;Ito;K
• 通讯作者: K

Injury prediction based on classifications of vehicle acceleration waveforms in frontal crashes

基于车辆正面碰撞加速度波形分类的伤害预测

• 发表时间: 2014
• 期刊: International Journal of Crashworthiness
• 影响因子: 1.9
• 作者: Maika Katagiri;Yusuke Miyazaki;Sadayuki Ujihashi;Takashi Fukaya;Keiichi Ito;Hiroyuki Suzuki and Toshiro Okuyama
• 通讯作者: Hiroyuki Suzuki and Toshiro Okuyama

前面衝突時の車両加速度波形の分類に基づく傷害予測

基于车辆加速度波形分类的正面碰撞损伤预测

• 发表时间: 2013
• 作者: 片桐 麻衣佳;宮崎 祐介（東京工業大学）;宇治橋 貞幸（日本文理大学）;深谷 敬;伊藤 敬一;鈴木 祐之;奥山 敏郎（三菱自動車工業）
• 通讯作者: 奥山 敏郎（三菱自動車工業）

Classification of Crash Pattern Based on Vehicle Acceleration and Prediction Algorithm for Occupant Injury

基于车辆加速度的碰撞模式分类及乘员伤害预测算法

• 发表时间: 2013
• 期刊: International Journal of Society of Automotive Engineers
• 作者: Katagiri;M.;Tsubouchi;H.;Ujihashi;S.;Fukaya;T.;Awano;M.;Ito;K.;Suzuki;H .;Okuyama;T
• 通讯作者: T

車載センサデータに基づく事故時の車体変形判別手法の構築

开发一种基于车载传感器数据确定事故期间车身变形的方法

• 发表时间: 2014
• 作者: 内田 忠宏;宮崎 祐介;三浦 祐司（東京工業大学）;宇治橋 貞幸（日本文理大学）;深谷 敬;堂免 弘樹;奥山 敏郎;前田 雅史（三菱自動車工業）
• 通讯作者: 前田 雅史（三菱自動車工業）