Citation

Ejima S, Goto T, Zhang P, Cunningham K, Wang S. Trans. Soc. Automot. Eng. Jpn. 2022; 53(6): 1233-1238.

Copyright

(Copyright © 2022, Society of Automotive Engineers of Japan)

DOI

10.11351/jsaeronbun.53.1233

PMID

unavailable

Abstract

An injury severity prediction algorithm for AACN was developed using a logistic regression model to predict the probability of sustaining an Injury Severity Score (ISS) 15+ injury. National Automotive Sampling System Crashworthiness Data System (NASS-CDS: 1999-2015) and model year 2000 or later were filtered for new case selection criteria, based on vehicle body type, to match SUBARU vehicle category. Moreover, presence of the right-front passenger and its interaction with crash direction were considered, which affected risk prediction significantly especially in the side-impact crashes. Variable selection techniques were used to construct the final ISP algorithm with relevant features. In this paper, we presented results of injury prediction algorithms, which do consider the effect of a right-front passenger were proposed. The area under the receiver operator characteristic curve (AUCs) was used as the metric to evaluate model performances, AUC was 0.862 with the model for cross-validation. Delta-V, seat belt use, and crash direction were important predictors of serious injury, and moreover, the presence of right-front passenger was a significant injury risk modifier, especially for side impact crashes.

Language: ja

Keywords

AACN; Injury prediction; Occupant protection; Safety