Occupant-Based Injury Severity Prediction

 Authors: Susan H. Owen and Jeffrey W. Joyner—Global Product Safety & Systems, General Motors; Peng Zhang and Stewart C. Wang—University of Michigan International Center for Automotive

Abstract

Road traffic injuries continue to be a leading cause of death around the world. Rapid emergency response is a key factor in improving occupant outcomes. Over the past ten years, Injury Severity Prediction (ISP) models have been developed and deployed to assist in effective dispatch of emergency medical services (EMS). Prior versions of ISP have relied on driver-based scenarios that are not relevant in many of the possible autonomous vehicle (AV) contexts. This paper describes the development and validation of occupant-based ISP models that predict injury severity for specific vehicle seat positions. Models show improved predictive performance, sensitivity 80% and specificity over 95%, for front row occupants. Second row occupant models have similar specificity, but sensitivity scores dropped due to occupant heterogeneity and small sample sizes of seriously injured occupants.

Type: Full Paper

Keywords: AACN, ISP, injury severity prediction, emergency response

© Stapp Association, 2021

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