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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.

A simple spring-mass model of the impact response of the side impact dummy (SID) is established. The spring and mass constants of the model are established through system identification methodology based on data from impact tests. The tests are performed in laboratory with hydraulically driven impactors impacting the chest and pelvis of the SID. The input data to the model consist of measured contact force or impactor velocity time histories, and the output data are accelerations on the rib, spine, and pelvis of the SID. The established model appears to predict the test results with reasonable accuracy. The main purpose of this study, however, is to use this simple model to carry out parametric studies of the response of the dummy with changing impact parameters, the result of which would be useful in understanding vehicle crash tests using the SID.

For the purposes of analyzing and understanding the general effects of a set of different vehicle attributes on overall crash outcome a fleet model is used. It represents the impact response, in a one-dimensional sense, of two vehicle frontal crashes, across the frontal crash velocity spectrum. The parameters studied are vehicle mass, stiffness, intrusion, pulse shape and seatbelt usage. The vehicle impact response parameters are obtained from the NCAP tests. The fatality risk characterization, as a function of the seatbelt use and vehicle velocity, is obtained from the NASS database. The fatality risk is further mapped into average acceleration to allow for evaluation of the different vehicle impact response parameters. The results indicate that the effects of all the parameters are interconnected and none of them is independent. For example, the effect of vehicle mass on fatality risk depends on seatbelt use, vehicle stiffness, available crush, intrusion and pulse shape.

The response of the spine acceleration to rib and pelvis acceleration input of the side impact dummy (SID) is modeled using system identification methods. The basis for the modeling is a simplified representation of the SID by a 3-mass, 2-spring system. Based on this spring-mass representation, two types of response models are established. The first is a "gray-box" type with rib/pelvis-spine relationship modeled by Auto Regression with eXogeneous (or eXtra) input (ARX) type system models. The structure of these models is partially based on the spring-mass simplified representation, hence the notion "gray- box." The parameters of these models are identified through linear regression from test data. The second type of models is noted "physical model" here, since it is strictly a state- space form of the equation of motion of the simple spring-mass representation.

The objective of this study is to analytically model the fatality risk in frontal vehicle-to-vehicle crashes of the current vehicle fleet, and its sensitivity to vehicle mass change. A model is built upon an empirical risk ratio-mass ratio relationship from field data and a theoretical mass ratio-velocity change ratio relationship dictated by conservation of momentum. The fatality risk of each vehicle is averaged over the closing velocity distribution to arrive at the mean fatality risks. The risks of the two vehicles are summed and averaged over all possible crash partners to find the societal mean fatality risk associated with a subject vehicle of a given mass from a fleet specified by a mass distribution function. Based on risk exponent and mass distribution from a recent fleet, the subject vehicle mean fatality risk is shown to increase, while at the same time that for the partner vehicles decreases, as the mass of the subject vehicle decreases.

Simple rigid body dynamics models are created to analyze the curb- and soil-trip types of rollover events and experimental methods that are used to simulate these events. Equations for the models are given, and they are integrated numerically to obtain the solution. Solutions of the models provide a break down of the energy during these events, which exposes the importance of energy absorption, unloading, and friction during the impact-and-roll process. Furthermore, the models are used to derive the critical sliding velocity under different test parameters. They are also used to understand near-critical state responses of the vehicle, and the corresponding characteristics of the signals in the phase space.