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An assessment tool was developed for rollover CAE signals evaluation to assess primarily the qualities of CAE generated sensor waveforms. This is a key tool to be used to assess CAE results as to whether they can be used for algorithm calibration and identify areas for further improvement of sensor. Currently, the method is developed using error estimates on mean, peak and standard deviation. More metrics, if necessary, can be added to the assessment tool in the future. This method has been applied to various simulated signals for laboratory-based rollover test modes with rigid-body-based MADYDO models.

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.

CAE has played a key role in development of the rollover safety technology by reducing the required number of prototypes. CAE-led Design Of Experiments (DOE) studies have helped in developing the process to minimize the number of CAE runs and to optimize use of the prototypes. This paper demonstrates the use of CAE/DOE for the design and optimization of rollover vehicle prototypes and also investigates effects of various factors in the selection of vehicle configuration for rollover sensor calibration testing. The process described herein has been successfully applied to vehicle programs. Modeling and analysis guidelines are also presented for CAE engineers to help in optimizing vehicle prototypes at program level.

This paper presents an image analysis of a laboratory-based rollover crash test using camera-matching photogrammetry. The procedures pertaining to setup, analysis and data process used in this method are outlined. Vehicle roll angle and rate calculated using the method are presented and compared to the measured values obtained using a vehicle mounted angular rate sensor. Areas for improvement, accuracy determination, and vehicle kinematics analysis are discussed. This paper concludes that the photogrammetric method presented is a useful tool to extract vehicle roll angle data from test video. However, development of a robust post-processing tool for general application to crash safety analysis requires further exploration.

In today's automotive market, rollover protection systems are drawing an increasing attention. Unlike in conventional frontal and side impact events where linear accelerometers are used to detect the crash severity and make decision on deployment/non-deployment of the air bag restraint systems, rollover events require a new detection method using angular rate sensors. In the past, various angular rate sensors have been used in navigation systems to calculate pitch angle, or in camcorders for image stabilization. These sensors work at a low dynamic operating range, i.e. less than 100 degrees/sec. Angular rate sensors in automotive applications demand a higher dynamic capacity greater than 250 degrees/sec. (or °/s). This paper reviews automotive grade angular rate sensors currently being introduced for rollover protection system applications.