| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2011: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Research Abstract |
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.
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