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Mathematical models in combination with mechanical tests were used to develop a frontal crash sensing system and algorithm. The required sensor closure time for the initiation of driver side airbag deployment was estimated by means of multi body dynamics occupant models. The crash sensing system and algorithm were developed using predictions from a finite element model of the front structure of a passenger vehicle. All models were validated by means of mechanical tests. Generally good agreement was obtained between the predictions from the models and the results from the mechanical tests. In the sensor closure time analysis two occupant sizes were used, the 50%-ile male and the 5%-ile female occupant. Two restraint conditions were evaluated, no belt and a belt system incorporating a pretensioner. A number of crash pulses of various severity were used.

The literature contains a wide range of response data describing the biomechanics of isolated body regions. Current data for the validation of frontal anthropomorphic test devices and human body computational models lack, however, a detailed description of the whole-body response to loading with contemporary restraints in automobile crashes.

Whole-body kinematics of the finite element human body model THUMS was evaluated by means of sled tests. A model of a crash test dummy (Hybrid-III 50%-ile) was used to validate the test environment by matching the model predictions to the experimentally measured dummy sled test responses. Once the environment was validated, the THUMS model was placed in the sled model and the post mortem human subject (PMHS) sled tests were replicated. Two test configurations were used for the evaluation. One configuration was high impact velocity sled tests with an advanced restraint system. The other configuration was low impact velocity sled tests with a basic restraint system. The test velocities were 48 km/h and 29 km/h respectively. The evaluation was carried out by an objective rating method that compared predictions from the model to results from the mechanical tests. The method assessed the peak level, peak timing and curve shapes of the predictions relative to the test results.