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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.

Regression models are used to understand the relative fatality risk for drivers in front-front and front-left crashes. The field accident data used for the regressions were extracted by NHTSA from the FARS database for model years 2000-2007 vehicles in calendar years 2002-2008. Multiple logistic regressions are structured and carried out to model a log-linear relationship between risk ratio and the independent vehicle and driver parameters. For front-front crashes, the regression identifies mass ratio, belt use, and driver age as statistically significant parameters (p-values less than 1%) associated with the risk ratio. The vehicle type and presence of the ESC are found to be related with less statistical significance (p-values between 1% and 5%). For front-left crashes the driver risk ratio is also found to have a log-log linear relationship with vehicle mass ratio.

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.

The object of this paper is to highlight the potential limitations of numerical procedures and the need to capture the relevant physics in the FEA models for head impact studies. This is accomplished through a discussion on stress update objectivity, which assumes particular importance because it affects the accuracy of stress and strain calculations when large displacements associated with rotations, as seen in head impacts, are involved. Inaccurate stress and strain results will also result due to material rotation if the objectivity is not maintained.