Bayerische Motoren Werke Aktiengesellschaft

Andreas Kuhn

Michael Holzner

Martin Peller

Jan Urbahn

Peter Bachmaier

Petra Foith

Like in a conventional crash detection algorithm, a crash is detected if a threshold value of an acceleration or an integrated acceleration is exceeded. Various characteristic values for characterizing the sensor signal paths are collected and supplied to the neural network. Said neural network returns the characteristic values for the seriousness of the crash, thereby facilitating the various retention systems to reaction individually. The invention provides a method for reliably, simply and quickly making statements regarding the seriousness and the course of a motor vehicle crash and for controlling occupant protection systems according to demand.

Method for judging the seriousness of a motor vehicle crash

In a method for recognizing the severity of a vehicle collision, wherein the output signal of one or more acceleration sensors is processed and fed to a neural network that controls a release unit for an occupant protection device, and further wherein several occupant protection devices can be selected by the release unit in accordance with the severity and course of the vehicle collision, the future time progression of the output signal of the acceleration sensor is predicted with the help of the neural network based on the acceleration sensor signal values during at least one defined point in time.

Method for recognizing the severity of a vehicle collision

A method is provided for determining the severity and pattern of a vehicle collision. A plurality of crash sensors are located in various points in the vehicle. Output signals from the crash sensors are fed to an evaluation unit. The output signals of the crash sensors are related to a system of coordinates that is invariable relative to the vehicle. Information about the movement of the sensor locations and the system of coordinates is obtained from the output signals.

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Munich, Germany

Andreas Kuhn