Method for predicting impact and an impact prediction system for realizing the same by using neural networks

Claim 1

A method for predicting impact by using neural networks comprising steps of: supplying a predetermined crash curve as a first group of parallel training data to a first neural network having an intermediate layer to train said first neural network by means of a learning calculation of a back propagation network architecture and supplying a predetermined air bag deployment limit curve as a second group of training data to a second neural network to train said second neural network by a back propagation network architecture; supplying data indicative of a crash curve obtained by an acceleration sensing device when collision occurs as a set of data having a predetermined time interval to said first and second neural networks; predicting, based on initial data of the crash curve obtained from said first neural network, a displacement output from the initial data of the crash curve on the basis of the training result in said first neural network, and obtaining, based on the displacement output from the initial data, a time to reach the threshold for maximum occupant displacement in which an air bag can be opened to protect an occupant,; comparing in said second neural network data indicative of the crash curve on said collision in accordance with the training result in said second neural network and data obtained from the air bag deployment limit curve, and outputting a decision signal indicative of deploying the air bag only when the data for the crash curve are greater than the data from the air bag deployment limit curve; and supplying an air bag deployment operation command signal only when the time to reach the threshold for maximum occupant displacement is obtained in the first neural network and when the second neural network outputs the decision signal indicative of deploying the air bag.

Claim 2

A method for predicting impact by using neural networks comprising steps of: supplying a predetermined crash curve as a first group of parallel training data to a first neural network having an intermediate layer to train said first neural network by means of a learning calculation of a back propagation network architecture and supplying a predetermined air bag deployment limit curve as a second group of training data to a second neural network to train said second neural network by a back propagation network architecture; supplying data indicative of a crash curve obtained by an acceleration sensing device when collision occurs as a set of data having a predetermined time interval to said first and second neural networks; predicting, based on initial data of the crash curve obtained from said first neural network, a displacement output from the initial data of the crash curve on the basis of the training result in said first neural network, and obtaining, based on the displacement output from the initial data, a time to reach the threshold for maximum occupant displacement in which an air bag can be opened to protect an occupant,; comparing in said second neural network data indicative of the crash curve on said collision in accordance with the training result in said second neural network and data obtained from the air bag deployment limit curve, and outputting a decision signal indicative of deploying the air bag only when the data for the crash curve are greater than the data from the air bag deployment limit curve; and supplying an air bag deployment operation command signal only when the time to reach the threshold for maximum occupant displacement is obtained in the first neural network and when the second neural network outputs the decision signal indicative of deploying the air bag.

Claim 6

A system for predicting impact by using neural networks comprising: displacement prediction means where crash curve data obtained on collision by an acceleration sensing device are supplied, as a data set having a predetermined time interval, to a first neural network having an intermediate layer already trained by means of a learning calculation of a back propagation network architecture and supplied with a predetermined training crash curve as a first group of parallel training data, said first neural network predicting a displacement output after initial crash curve data and obtaining a time to reach a threshold for maximum occupant displacement in accordance with the training result in said first neural network; air bag deployment decision means where said crash curve data obtained on collision are supplied to a second neural network having an intermediate layer already trained by means of a learning calculation of a back propagation network architecture and supplied with a predetermined air bag deployment limit curve as a second group of training data, said second neural network comparing data indicative of the crash curve on said collision in accordance with the training result in said second neural network and data obtained from the air bag deployment limit curve and outputting a decision signal indicative of deploying an air bag only when the data for the crash curve are greater than the air bag deployment limit curve; and a logic circuit where an air bag deployment operational command signal is supplied to air bag deployment operation means only when the time to reach the threshold for maximum occupant displacement is obtained in the first neural network and when the second neural network outputs the decision signal indicative of deploying the air bag.

Claim 7

A system for predicting impact by using neural networks comprising: displacement prediction means where crash curve data obtained on collision by an acceleration sensing device are supplied, as a data set having a predetermined time interval, to a first neural network having an intermediate layer already trained by means of a learning calculation of a back propagation network architecture and supplied with a predetermined training crash curve as a first group of parallel training data, said first neural network predicting a displacement output after initial crash curve data and obtaining a time to reach a threshold for maximum occupant displacement in accordance with the training result in said first neural network; air bag deployment decision means where said crash curve data obtained on collision are supplied to a second neural network having an intermediate layer already trained by means of a learning calculation of a back propagation network architecture and supplied with a predetermined air bag deployment limit curve as a second group of training data, said second neural network comparing data indicative of the crash curve on said collision in accordance with the training result in said second neural network and data obtained from the air bag deployment limit curve and outputting a decision signal indicative of deploying an air bag only when the data for the crash curve are greater than the air bag deployment limit curve; and a logic circuit where an air bag deployment operational command signal is supplied to air bag deployment operation means only when the time to reach the threshold for maximum occupant displacement is obtained in the first neural network and when the second neural network outputs the decision signal indicative of deploying the air bag.