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A vehicle integrated sensing and analysis system has been designed, implemented, and demonstrated to nonintrusively monitor several biological signals of the driver. The biological driver signals measured by the system are the heart electrical signals or pseudo Lead-I electrocardiography (pLI-ECG), the galvanic skin response (GSR) or electrical conductance measured from the driver's fingers to palm, the palm skin temperature, the face skin temperature, and the respiration rate. The pLI-ECG and GSR measurements are made through direct contact of the driver hands with stainless steel electrodes integrated in the steering wheel rim. The temperature measurements are made with non-contacting infrared temperature sensors, also located on the steering wheel. The respiration rate was measured using a flexible thin film piezoelectric sensor affixed to the seatbelt.

State of the art electronic restraint systems rely on the acceleration measured during a vehicle crash for deployment decisions. The acceleration signal is analyzed with different criteria, among which the velocity change is a dominant criterion in almost any existing crash detection algorithm. Sensors in the front crush zone have recently been added to help develop restraint systems that comply with the new FMVSS208 and EuroNCAP regulations. Front crash sensors are usually evaluated for their velocity change during a crash and typically play a key role in the deployment decision. CAE based FEA analysis has recently been used to generate signals at the sensor module locations in crash simulations to provide supplemental information for crash sensing algorithm development and calibration. This paper presents an initial effort in developing a velocity-based crash detection algorithm, that allows broad use of CAE generated velocity time histories for system calibration.

Development of frontal impact airbag sensor algorithms/calibrations requires crash signals, which can be obtained from vehicle crash testing and/or CAE simulations. This paper presents the development of finite element sensor models to generate CAE simulated crash pulses/signals at the sensing location during frontal impacts. These signals will be evaluated for potential used in the airbag sensor algorithm/calibration.

This paper describes the development of a simulation methodology to predict body mount functional stiffness. The method provides some improvement over the one previously developed by Cheng and Le. Advantages of this method are 1) user's expertise is not a requirement, 2) elimination of trial-and-error runs for tuning the stiffness characteristics and 3) a predictive tool applicable prior to crash tests. Verification of the method is carried out by comparing the numerical results with those obtained from trial-and-error method and tests. The method leads to promising results in full vehicle crash simulations.