Automotive Systems Laboratory, Inc.

Automatic Systems Laboratory, Inc.

Michael A Piskie

Tony Gioutsos

Daniel N Tabar

In a system and method for controlling actuation of a vehicle passenger safety device, wherein a first time-varying measure m.sub.1 (t), itself a function of received vehicle acceleration information, is accumulated to obtain a second time-varying measure m.sub.2 (t) for subsequent comparison with a threshold value therefor, the first measure m.sub.1 (t) is 'damped' prior to accumulation by subtracting therefrom a correction value or 'damping factor f.sub.d.' The damping factor f.sub.d may be either a constant or a time-varying function f.sub.d (t) of one or more other time-varying measures, themselves based on received vehicle acceleration information.

Method for damping crash-discrimination measures

A vehicle crash discrimination method and system for controlling the actuation of a vehicle passenger restraint device (e.g., an air bag or a seat belt harness) comprises determining a vehicle velocity value and comparing it to a predetermined velocity threshold value. If the predetermined velocity threshold value is exceeded, the slope of vehicle acceleration with respect to time (i.e. jerk value) is determined and compared to a predetermined slope threshold value. The passenger restraint device is actuated if the predetermined slope threshold value is exceeded. The present method accurately discriminates between low-velocity crashes in which actuation of the passenger restraint device is not desired and low-velocity, long-time -period crashes in which actuation is desired by setting the velocity threshold equal to approximately half the velocity of a maximum allowable crash in which actuation of the passenger restraint device is not desired.

Method for discriminating long-period, low-velocity crashes

A system (10) for simulating vehicle crashes for testing or evaluating the reliability of a vehicle crash sensor (20) includes a model waveform generator (16) which utilizes a method of modeling an actual vehicle crash waveform to generate an infinite set of model crash waveforms, and subsequently inputs these sets of modeled crash waveforms into a thruster apparatus (12). The thruster apparatus (12) mechanically exerts a force onto the crash sensor (20) corresponding to each input model waveform. The crash sensor response is tracked and analyzed by a test analyzer (18). The model crash waveforms are generated by breaking down a predetermined crash waveform into a crash pulse (signal of interest) and multiplicative noise signal. The multiplicative noise signal is then statistically characterized as a function of time. Randomly variable waveforms from a white noise generator (128) are then modified with the statistical characterization and multiplied with the signal of interest to generate the infinite set of model crash waveforms.

Vehicle crash simulator system for testing crash sensors

A system and method for triggering deployment of a vehicle air bag in response to a crash or sudden vehicle deceleration responsive to values for modified velocity, predicted acceleration, high-frequency velocity, and high-variance velocity. The air bag is triggered when at least two of the values for modified velocity, predicted acceleration, high-frequency velocity, and high-variance velocity exceed their respective thresholds.

System and method for actuating vehicle safety device using damped

A method of modeling a vehicle crash waveform to generate an infinite set of crash waveforms comprises breaking down a predetermined crash waveform into a crash pulse (signal of interest) and multiplicative noise signal. The multiplicative noise signal is then statistically characterized as a function of time. Randomly variable waveforms from a white noise generator are then modified with the statistical characterization and multiplied with the signal of interest to generate the infinite set of model crash waveforms. These model crash waveforms can then be used to evaluate the performance of crash detection system in accordance with a 'Monte Carlo' probability determination technique.

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Bloomfield Hills, MI, United States of America

Michael A Piskie