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This paper reports a study on the use of response inverse filtering (RIF) methodology for crash pulse prediction. RIF is based on the finite impulse response (FIR) and inverse filtering (IF) methods. The FIR coefficients obtained by the digital convolution theory and the least squared error approach serve to transfer response from the input (impacting or excitation) side to the output (non-impacting or receiving) side. The FIR method, a process of low pass filtering (e.g. truck body mount), is commonly used in predicting the non-impacting side (e.g. truck body or cab) response with the input excitation in the impacting side (e.g. truck frame). The accuracy in the validation and prediction via FIR transfer function depends on the frequency contents of the input and output accelerometer data from which the transfer function is developed. The prediction accuracy is low if the output data contain higher frequency components than the input.

Computer simulations of occupant dynamics in frontal crashes have pretty much been done like sled tests, in that crash-induced deformation of the interior — which may be significant for the occupant — has usually not been accounted for. The object of occupant dynamics simulation studies is often to assess the effect of changes in vehicle front-end parameters on occupant response. But these parameter variations may influence the amount of interior deformation. In order to simulate more accurately occupant dynamics in very severe crashes, the interior deformation caused by engine intrusion should concurrently be simulated. Crash test results over a range of speeds were used in a computer simulation study of occupant compartment intrusion in high speed barrier crash tests. It was found that intrusion has a significant effect on occupant response and where appropriate should be included in crash simulations.