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A statistical theory is used to quantify the amount of information transmitted from a transducer (i.e., accelerometer) to the air bag controller during a vehicle crash. The amount of information relevant to the assessment of the crash severity is evaluated. This quantification procedure helps determine the effectiveness of different testing conditions for the calibration of sensor algorithms. The amount of information in an acceleration signal is interpreted as a measure of the ability to separate signals based on parameters that are used to assess the severity of an impact. Applications to a linear spring-mass model and to actual crash signals from a development vehicle are presented. In particular, the comparison of rigid barrier (RB) and offset deformable barrier (ODB) testing modes is analyzed. Also, the performance of front-mounted and passenger compartment accelerometers are compared.

One way to improve vehicle's fuel economy is to reduce its weight. Reducing weight, however has other consequences. One of these is reduced vehicle size. Almost invariably, lighter vehicles are smaller. Reducing vehicle weight has also been associated with a reduction in occupant protection; the lighter the vehicle, the greater the chance of injury when a crash occurs. For this study, a data-based model is used to evaluate the independent effects of size and weight. This model is constructed using the NASS database and information obtained from NCAP tests. The results indicate that although mass is the dominant factor, size also has an effect; some of the observed reduction in safety benefits associated with mass reduction is actually an effect of size reduction. The model is also used to evaluate the effects of varying stiffness.