Transactions of Society of Automotive Engineers of Japan
Online ISSN : 1883-0811
Print ISSN : 0287-8321
ISSN-L : 0287-8321
Construction of Injury Prediction Model for Car Occupants using Gradient-Boosting Decision Tree Model
Keita TakahashiYusuke MiyazakiKoji KitamuraFusako Sato
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2024 Volume 55 Issue 1 Pages 56-62

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Abstract
It is necessary to estimate the injury of occupants during car accidents to estimate the effect of injury reduction performance of autonomous driving systems. Although there are some estimation models of injury of occupants based on logistic regression, logistic regression has the problem of being unable to express nonlinear relationships between explanatory and objective variables. In this study, we used LightGBM, a decision tree model, and our own selected explanatory variables to construct an injury prediction model to predict the probability of VAIS3+ of vehicles. It showed a significant improvement in performance from URGENCY.
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© 2024 Society of Automotive Engineers of Japan, Inc.
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