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Current state-of-the-art vehicles implement pedestrian protection features that rely on pedestrian detection sensors and algorithms to trigger when impacting a pedestrian. During the development phase, the vehicle must “learn” to discriminate pedestrians from the rest of potential impacting objects. Part of the training data used in this process is often obtained in physical tests utilizing legform impactors whose external biofidelity is still to be evaluated. This study uses THUMS as a reference to assess the external biofidelity of the most commonly used impactors (Flex-PLI, PDI-1 and PDI-2). This biofidelity assessment was performed by finite element simulation measuring the bumper beam forces exerted by each surrogate on a sedan and a SUV. The bumper beam was divided in 50 mm sections to capture the force distribution in both vehicles. This study, unlike most of the pedestrian-related literature, examines different impact locations and velocities.

Biomechanical data are often assumed to be doubly censored. In this paper, this assumption is evaluated critically for several previously published sets of data. Injury risk functions are compared using simple logistic regression and using survival analysis with 1) the assumption of doubly censored data and 2) the assumption of right-censored (uninjured specimens) and uncensored (injured) data. It is shown that the injury risk functions that result from these differing assumptions are not similar and that some experiments will require a preliminary assessment of data censoring prior to finalizing the experimental design. Some types of data are obviously doubly censored (e.g., chest deflection as a predictor of rib fracture risk), but many types are not left censored since injury is a force-limiting phenomenon (e.g., axial force as a predictor of tibia fracture). Guidelines for determining the censoring for various types of experiment are presented.