Accurate Predictive Algorithm for Air Bag Expansion by Fusing the Conventional Predictive Algorithm and Proximity Sensor

Nao Kitada; Kajiro Watanabe

doi:10.4271/980907

1998-02-23

Accurate Predictive Algorithm for Air Bag Expansion by Fusing the Conventional Predictive Algorithm and Proximity Sensor 980907

The airbag systems in the first generation had been developed and are equipped in real automobiles. This paper is aimed at describing a new airbag scheme that might be categorized in the 1.5 generation.

The airbag system always needs some delay time between the triggering and complete expanding. The existence of the delay time is the main cause of difficulty for accurate airbag triggering. The predictive airbag expanding algorithm that compensates the delay time was proposed and the validity was examined in the first generation development.

Development of the 1.5 generation airbag systems with the higher performance are our next problem. Airbag equipped in automobiles must receive driver's body at the optimal timing when collision by which the effect of airbag is extremely improved. The more accurate predictive airbag system is required.

The algorithm combines the signal from the conventional acceleration sensor and that from a proximity sensor which provides the preview information of collision, and provides the accurate timing. The application of Kalman filter to the signal from the acceleration sensor yields noise suppressed acceleration of the movement of driver. The predictive algorithm based only on the signal from the acceleration sensor can predict driver's motion only after collision. The assist of the proximity sensor to the conventional predictive algorithm provides the prediction before and after collision.

Here we theoretically consider how the use of proximity sensor improves the prediction accuracy. We carry out simulations to show the validity of the new method.

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Accurate Predictive Algorithm for Air Bag Expansion by Fusing the Conventional Predictive Algorithm and Proximity Sensor 980907

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