Comparison of Vehicle-Based Crash Severity Metrics for Predicting Occupant Injury in Real-World Oblique Crashes

Abstract

The flail space model (FSM) is currently used in U.S. roadside hardware crash testing as a means of assessing occupant injury risk using observed vehicle kinematics data. European roadside hardware crash tests use an FSM variant along with a variant of the acceleration severity index (ASI). Although the FSM and ASI are currently used in roadside hardware testing, other vehicle-based crash severity metrics exist. Previous research has focused on examining the ability of these metrics to predict injury in frontal crashes. Despite the Manual for Assessing Safety Hardware prescribing a significant number of oblique crash tests, there has been little research on how well these metrics predict real-world oblique crash injury. This study compared the ability of six different vehicle-based metrics to predict occupant injury in oblique crashes: maximum delta-v, occupant impact velocity, ridedown acceleration, ASI, occupant load criterion, and vehicle pulse index. The crash severity metrics were calculated from real-world crash pulse data recorded by event data recorders. Oblique crashes from the National Automotive Sampling System Crashworthiness Data System were used to train logistic regression models that predict moderate to fatal injuries. The models were then compared on a dataset of oblique crashes from the Crash Investigation Sampling System. The results of this study confirmed that vehicle-based metrics provide a reasonable means of predicting real-world occupant injury risk in oblique crashes and suggest little difference between the investigated metrics. In addition to the vehicle-based metrics, belt use and vehicle damage location were found to influence injury risk.

Keywords

infrastructure roadway design roadside safety design MASH safety general crash severity

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