Pattern recognition techniques, such as neural networks, have been applied to identify objects within the passenger compartment of the vehicle, such as a rear facing child seat or an out-of-position occupant, and to suppress the airbag when an occupant is more likely to be injured by the airbag than by the accident. Neural networks have also been applied to sense automobile crashes. The use of neural networks is extended here to tailoring the airbag inflation to the severity of the crash, the size, position and relative velocity of the occupant and other factors such as seatbelt usage, seat and seat back positions, vehicle velocity, and any other relevant information. Crash sensors can predict that a crash is of a severity which requires the deployment of an airbag for the majority of real world crashes. A more difficult problem is to predict the crash velocity versus time function and then to adjust the airbag inflation/deflation over time so that just the proper amount of gas is in the airbag at all times even without considering the influence of the occupant. To also simultaneously consider the occupant size, weight, position and velocity renders this problem unsolvable by conventional methods. (A)
Samenvatting