US5541590A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Vehicle crash predictive and evasive operation system by neural networks
Assignee: Takata Corp
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Filing: 1995-01-19
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Granted: 1996-07-30
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Expires: 2015-01-19
Abstract
Abstract A system for predicting and evading crash of a vehicle includes an image pick-up device mounted on the vehicle for picking up images of actual ever-changing views when the vehicle is on running to produce actual image data, a crash predicting device associated with said image pick-up device, said crash predicting device being successively supplied with the actual image data for predicting occurrence of crash between the vehicle and potentially dangerous objects on the roadway to produce an operational signal when there is possibility of crash and a safety drive ensuring device connected to said crash predicting device for actuating, in response to the operational signal, an occupant protecting mechanism which is operatively connected thereto and equipped in the vehicle. The crash predicting device includes a neural network which is previously trained with training data to predict the possibility of crash, the training data representing ever-changing views previously picked-up from said image picking-up device during driving of the vehicle for causing actual crash.
Inventors
- 👤 Tomoyuki Nishio
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-24 05:56
CPC Classifications
B; B60; B60R; B60R16/00; B60R16/02; B60R16/023; B60R16/0231; B60T; B60T7/00; B60T7/12; B60T7/22; B60W; B60W10/00; B60W10/04; B60W10/18; B60W10/20; B60W30/00; B60W30/08; B60W30/09; B60W30/095; B60W50/00; B60W50/08; B60W50/14; B62; B62D; B62D15/00; B62D15/02; B62D15/025; B62D15/0265; B62D6/00; G; G06; G06N; G06N3/00; G06N3/02; G06N3/08; B60W2050/146; B60W2520/00; B60W2520/10; B60W2540/00; B60W2540/18; B60W2710/00; B60W2710/06; B60W2710/0605; B60W2710/18; B60W2710/20; B60Y; B60Y2300/00; B60Y2300/08; B60Y2300/09; Y; Y02; Y02T; Y02T10/00; Y02T10/80; Y02T10/84; Y10; Y10S; Y10S706/00; Y10S706/902; Y10S706/903; Y10S706/905
Claims Analysis
Total Claims: 5
Claim 1
Independent Claim
234 words
A system for predicting and evading crash of a vehicle comprising: image pick-up means mounted on the vehicle for picking up images of actual views in a direction of running of the vehicle while running of the vehicle, crash predicting means having a neural network, said neural network containing previously taken image data formed of successive scenes for causing accidents and being trained by a back propagation method for recognizing conditions in image data which cause said accidents, said neural network having an input layer formed of processing elements arranged parallel to each other, said input layer continuously receiving actual image data obtained from the image pick-up means, said neural network receiving the actual image data obtained from the image pick-up means while running of the vehicle, evaluating the actual image data by comparing it to said previously taken image data for causing the accidents, judging if the vehicle is predicted to crash based on the comparison of said previously taken image data with an object noticed in the actual image data of the image pick-up means, and outputting an operational signal in case of prediction of occurrence of a crash with said object, and safety drive ensuring means connected to said crash predicting means, said safety drive ensuring means, in response to the operational signal, operating to evade the crash between the vehicle and the object for protecting an occupant of the vehicle.
Claim 2
Independent Claim
234 words
A system for predicting and evading crash of a vehicle comprising: image pick-up means mounted on the vehicle for picking up images of actual views in a direction of running of the vehicle while running of the vehicle, crash predicting means having a neural network, said neural network containing previously taken image data formed of successive scenes for causing accidents and being trained by a back propagation method for recognizing conditions in image data which cause said accidents, said neural network having an input layer formed of processing elements arranged parallel to each other, said input layer continuously receiving actual image data obtained from the image pick-up means, said neural network receiving the actual image data obtained from the image pick-up means while running of the vehicle, evaluating the actual image data by comparing it to said previously taken image data for causing the accidents, judging if the vehicle is predicted to crash based on the comparison of said previously taken image data with an object noticed in the actual image data of the image pick-up means, and outputting an operational signal in case of prediction of occurrence of a crash with said object, and safety drive ensuring means connected to said crash predicting means, said safety drive ensuring means, in response to the operational signal, operating to evade the crash between the vehicle and the object for protecting an occupant of the vehicle.
Claim 3
Dependent Claim
77 words
A system as claimed in claim 1, wherein said neural network further includes an output layer formed of a single processing element and connected to the processing elements of the input layer in series, said input layer instantaneously receiving said actual image data from the image pick-up means, and said output layer outputting a binary signal for indicating if said crash is predicted to occur in response to the actual image data inputted to the input layer.
Claim 4
Dependent Claim
92 words
A system as claimed in claim 2, wherein in a training of the neural network by the back propagation method, said input layer receives the previously taken image data formed of successive scenes for causing the accidents and receives said binary signal from said output layer indicating that the accidents occurred in said successive scenes, said neural network, during the driving of the vehicle after the training, evaluating said actual image data obtained from the image pick-up means to determine if it corresponds to said previously taken image data for causing accidents.
Claim 5
Dependent Claim
38 words
A system as claimed in claim 3, wherein said neural network containing trained data is memorized in a ROM for constituting the crash predicting means, said ROM being included in a circuit for the safety drive ensuring means.
References Cited (Backward Citations)
FR2554612A1
Cited
DE3837054A1
Cited
EP0358628A2
Cited
WO1990002985A1
Cited
US5189619A
Cited
US5200898A
Cited
US5130563A
Cited
DE4001493A1
Cited
US5162997A
Cited
JPH048639A
Cited
US5161632A
Cited
US5249157A
Cited
US5285523A
Cited
US5161014A
Cited
US5214744A
Cited
US5270708A
Cited
US5282134A
Cited
US5377108A
Cited
US5434927A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1999-07-30 | 2000-01-29 | 2000-07-30 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2003-07-30 | 2004-01-29 | 2004-07-30 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2007-07-30 | 2008-01-29 | 2008-07-30 | $7700.00 | Window Closed / Lapsed |