Method Development of Multi-Dimensional Accident Analysis Using Self Organizing Map

Uno, Hitoshi; Kageyama, Yusuke; Yamaguchi, Akira; Okabe, T.

doi:10.4271/2013-01-0758

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…In addition to various test conditions, there were different sizes of dummies and corresponding seating positions. In order to identify the kinematic parameters from such a wide variety of conditions to predict brain injury risk accurately, a comprehensive rational methodology is needed that enables extraction of some global common features to explain all of the test results and also simultaneously facilitates identification of specific characteristics matching local crash phenomena (6) .…”

Section: Introductionmentioning

confidence: 99%

Investigation of Brain Injury Mechanisms in Vehicle Crashes

Kikuchi¹,

Pal²,

Okabe³

2017

IJAE

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In recent years, an angular-velocity-based brain injury criterion, BrIC, has been proposed by the National Highway Traffic Safety Administration (NHTSA) for consumer vehicle safety assessment tests. In this study, the cumulative strain damage measure (CSDM), as one of the brain injury metrics, was calculated based on data obtained for a total of 360 anthropomorphic test devices (ATDs) in vehicle crash tests conducted by NHTSA and the Insurance Institute for Highway Safety (IIHS) using the Simulated Injury Monitor (SIMon ver. 4.0), a human brain finite element model developed by NHTSA's research institute. Self-Organizing Maps (SOMs) and hierarchical clustering were used to classify test data composed of the brain injury risk level based on CSDM and its corresponding head kinematic parameters. Results demonstrated that, in addition to the peak values of angular velocities, the peak values of angular accelerations around three axes are also influential parameters for accurately predicting brain injury risk based on CSDM.

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Section: Introductionmentioning

confidence: 99%