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Developing an effective side impact ATD for assessing vehicle impact responses requires a method for evaluating that ATD's bio-fidelity. ISO/TR9790 has been in existence for some years to serve that purpose. Recently, NHTSA sponsored a research project on the post-mortem human subjects (PMHS) responses subjected to side impact conditions. Based on those newly available PMHS data, Maltese generated a new approach for creating bio-fidelity corridors for human surrogates. The approach incorporates the time factor into the evaluation equation and automates the process (Maltese et al. 2002). This paper serves as the first attempt to look closely at the new bio-fidelity corridor generation process (hereafter referred as the Maltese approach) with respect to its validity, effectiveness, as well as its practicality. The effect of mass scaling was first examined in order to ensure the integrity of the data. The time alignment scheme and the formation of the corridors were then tested.

The effects of vehicle “stiffness” and mass on the occupant response during a crash may be determined by evaluation of accident data. However, “stiffness” and mass may be correlated, making it difficult to separate their effects. In addition, a single-valued “stiffness”, although well defined for linear case, is not well defined for non-linear systems, such as in vehicle crash, making the separation task even more difficult. One approach to addressing the lack of a clear definition of stiffness is to use multiple definitions. Each stiffness definition can then be correlated with mass to look for trends. In this study, such an approach was taken, and the different stiffness definitions were given and their values were obtained from rigid barrier crash test data. No clear relationship between mass and stiffness appears to exist. All the stiffness measures reviewed show, at best, only a weak correlation with mass. A stiffness analysis among different vehicle types was also carried out.

A new approach for improving estimates of the kinematic response of ATDs (anthropomorphic test devices) to vehicle crash events has been developed. This approach employs the Kalman Filter; a state model based estimation approach that has been widely applied to system dynamics problems ranging from navigation to missile guidance. The Kalman Filter approach combines measurements of crash event phenomena (acceleration and displacement), kinematic models of ATD behavior and statistics of sensor noise to create precise estimates of ATD motion during a crash. This paper presents an implementation of a state model and Kalman Filter for a sensor data collected from the chest of an ATD during an out-of-position airbag deployment test. Favorable comparisons are made between the Kalman Filter model approach and traditional methods involving numerical integration and differentiation.