US5583771A
Expired / Lapsed
US
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Method and apparatus for distinguishing between deployment events and non-deployment events in an SIR system
Assignee: Delphi Technologies Inc
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Filing: 1994-08-04
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Granted: 1996-12-10
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Expires: 2014-08-04
Abstract
Abstract A pattern recognition system is utilized in a supplementary inflatable restraint (SIR) system to distinguish between deployment and non-deployment events. The pattern recognition system preferably includes dedicated hardware or a microprocessor programmed to perform a neural network simulation utilizing crash data in the form of vehicle acceleration data. Training and trial vectors are generated from the data to train and, subsequently, test the neural network.
Inventors
- 👤 David D. Lynch
- 👤 James F. Long
- 👤 Rex P. Brumbach, Jr.
- 👤 Porfirio Garcia, Jr.
- 👤 Stephen J. Kiselewich
- 👤 Douglas D. Turner
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-25 17:21
CPC Classifications
B; B60; B60R; B60R21/00; B60R21/01; B60R21/013; B60R21/0132; B60R21/01332; B60R21/01338; G; G06; G06N; G06N3/00; G06N3/02; G06N3/04; G06N3/048; G06N3/08; G06N3/084; Y; Y10; Y10S; Y10S706/00; Y10S706/902; Y10S706/903; Y10S706/905
Claims Analysis
Total Claims: 22
Claim 1
Independent Claim
175 words
A method for distinguishing between deployment and non-deployment events from crash signals containing crash data in an SIR system for a motor vehicle, the method comprising the steps of: generating pairs of training vectors from the crash data, each pair of training vectors including a vector having input values and a vector having a known output value associated with either a deployment event or a non-deployment event; inputting the pairs of training vectors to a trainable pattern recognition system; recursively adjusting the pattern recognition system to converge to a configuration of the pattern recognition system to obtain a trained pattern recognition system for matching an input vector with either a deployment event or a non-deployment event; generating at least one trial input vector from the crash data, the at least one trial input vector corresponding to either a deployment event or a non-deployment event; and inputting the at least one trial input vector to the trained pattern recognition system to generate an output signal representation of one of the events corresponding to the input vector.
Claim 2
Independent Claim
175 words
A method for distinguishing between deployment and non-deployment events from crash signals containing crash data in an SIR system for a motor vehicle, the method comprising the steps of: generating pairs of training vectors from the crash data, each pair of training vectors including a vector having input values and a vector having a known output value associated with either a deployment event or a non-deployment event; inputting the pairs of training vectors to a trainable pattern recognition system; recursively adjusting the pattern recognition system to converge to a configuration of the pattern recognition system to obtain a trained pattern recognition system for matching an input vector with either a deployment event or a non-deployment event; generating at least one trial input vector from the crash data, the at least one trial input vector corresponding to either a deployment event or a non-deployment event; and inputting the at least one trial input vector to the trained pattern recognition system to generate an output signal representation of one of the events corresponding to the input vector.
Claim 3
Dependent Claim
13 words
The method of claim 1 wherein the crash data is vehicle acceleration data.
Claim 4
Dependent Claim
34 words
The method of claim 1 wherein the crash data is vehicle acceleration data and wherein the step of generating pairs of training vectors includes the step of converting the acceleration data to another format.
Claim 5
Dependent Claim
22 words
The method as claimed in claim 1 wherein the pattern recognition system includes a microprocessor programmed to perform a neural network simulation.
Claim 6
Dependent Claim
19 words
The method as claimed in claim 1 wherein the pattern recognition system includes dedicated hardware to perform pattern recognition.
Claim 7
Dependent Claim
21 words
The method as claimed in claim 1 wherein the pattern recognition system includes a microprocessor programmed to perform a back-propagation analysis.
Claim 8
Dependent Claim
28 words
The method as claimed in claim 1 wherein the trained pattern recognition system includes a neural network having an input layer, a hidden layer and an output layer.
Claim 9
Dependent Claim
40 words
The method as claimed in claim 7 wherein the input layer includes a plurality of neurodes and wherein the method further comprises the step of identifying a range defined by a minimum and a maximum for each of the neurodes.
Claim 10
Dependent Claim
46 words
The method as claimed in claim 8 wherein the step of recursively adjusting includes the step of adding new data to the input layer and clipping each of the neurodes of the input layer to fall within their respective ranges to obtain a clipped input layer.
Claim 11
Dependent Claim
33 words
The method as claimed in claim 1 wherein the step of generating pairs of training vectors includes the step of sampling the crash data at a sampling rate to obtain sampled crash data.
Claim 12
Dependent Claim
170 words
Apparatus for distinguishing between deployment and non-deployment events from crash data in an SIR system for a motor vehicle, the apparatus comprising: means for generating pairs of training vectors from the crash data, each pair of training vectors including a vector having input values and a vector having a known output value associated with either a deployment event or a non-deployment event; and a pattern recognition system for receiving the pairs of training vectors and for recursively adjusting to a configuration of the pattern recognition system to obtain a trained pattern recognition system for matching an input vector with either a deployment event or a non-deployment event; the generating means also generating at least one trial input vector from the crash data, the at least one trial input vector corresponding to either a deployment event or a non-deployment event; the at least one trial input vector being received by the trained pattern recognition system to generate an output signal representation of one of the events corresponding to the input vector.
Claim 13
Dependent Claim
170 words
Apparatus for distinguishing between deployment and non-deployment events from crash data in an SIR system for a motor vehicle, the apparatus comprising: means for generating pairs of training vectors from the crash data, each pair of training vectors including a vector having input values and a vector having a known output value associated with either a deployment event or a non-deployment event; and a pattern recognition system for receiving the pairs of training vectors and for recursively adjusting to a configuration of the pattern recognition system to obtain a trained pattern recognition system for matching an input vector with either a deployment event or a non-deployment event; the generating means also generating at least one trial input vector from the crash data, the at least one trial input vector corresponding to either a deployment event or a non-deployment event; the at least one trial input vector being received by the trained pattern recognition system to generate an output signal representation of one of the events corresponding to the input vector.
Claim 14
Dependent Claim
15 words
The apparatus as claimed in claim 11 wherein the crash data is vehicle acceleration data.
Claim 15
Dependent Claim
31 words
The apparatus as claimed in claim 11 wherein the crash data is vehicle acceleration data and wherein the generating means includes converting means for converting the acceleration data to another format.
Claim 16
Dependent Claim
22 words
The apparatus as claimed in claim 11 wherein the pattern recognition system includes a microcomputer programmed to perform a neural network simulation.
Claim 17
Dependent Claim
19 words
The apparatus as claimed in claim 11 wherein the pattern recognition system includes hardware dedicated to performing pattern recognition.
Claim 18
Dependent Claim
21 words
The apparatus as claimed in claim 11 wherein the pattern recognition system includes a microprocessor programmed to perform a back-propagation analysis.
Claim 19
Dependent Claim
28 words
The apparatus as claimed in claim 11 wherein the trained pattern recognition system includes a neural network having an input layer, a hidden layer and an output layer.
Claim 20
Dependent Claim
39 words
The apparatus as claimed in claim 17 wherein the input layer includes a plurality of neurodes and wherein the apparatus further comprises means for identifying a range defined by a minimum and a maximum for each of the neurodes.
Claim 21
Dependent Claim
47 words
The apparatus as claimed in claim 18 wherein the trained pattern recognition system includes means for adding new data to the input layer and means for clipping each of the neurodes of the input layer to fall within their respective ranges to obtain a clipped input layer.
Claim 22
Dependent Claim
28 words
The apparatus as claimed in claim 11 wherein the generating means includes sampling means for sampling the crash data at a sampling rate to obtain sample crash data.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1999-12-10 | 2000-06-10 | 2000-12-10 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2003-12-10 | 2004-06-10 | 2004-12-10 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2007-12-10 | 2008-06-10 | 2008-12-10 | $7700.00 | Window Closed / Lapsed |