Image processing system for dynamic suppression of airbags using multiple model likelihoods to infer three dimensional information
First Claim
1. An image processing system for use with an airbag deployment system having a seat, an occupant in the seat, a sensor for generating sensor measurements, an airbag, and an airbag controller, said image processing system comprising:
- an iterative tracking and predicting system, wherein said tracking and predicting system tracks and predicts the movement of an occupant in a seat by incorporating the most recent sensor measurement and past predictions into an estimate of the occupant'"'"'s position.
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Abstract
The present invention relates in general to systems used to determine whether airbag deployment should be disabled or modified due to the proximity of the occupant to the airbag. In particular, the present invention is an image processing system that uses an image and signal process that utilizes real-time streaming video-images from a video camera to determine if the occupant is too close to the air bag, or will be to close the air bag by the time that the airbag deploys. In a crash situation, the system quickly determines whether or not the airbag should be disabled or modified. The process uses a multiple-model Kalman filter to infer three-dimensional information from a sequence of two-dimensional images. The system predicts the position and shape of the occupant at a faster rate than the rate in which the camera collects data.
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Citations
21 Claims
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1. An image processing system for use with an airbag deployment system having a seat, an occupant in the seat, a sensor for generating sensor measurements, an airbag, and an airbag controller, said image processing system comprising:
an iterative tracking and predicting system, wherein said tracking and predicting system tracks and predicts the movement of an occupant in a seat by incorporating the most recent sensor measurement and past predictions into an estimate of the occupant'"'"'s position. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. An image processing system for use with an airbag deployment system having a seat, an occupant in the seat, an optical sensor, an airbag, a segmentation device, an airbag controller, said image processing system comprising:
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a tracking and predicting system, wherein said tracking and predicting system tracks a plurality of variables regarding an occupant;
an ARZ intrusion indicator, wherein said ARZ intrusion indicator determines which point of the occupant'"'"'s upper torso is closest to the airbag, and whether the occupant will be in a position within a predefined danger zone by the time the airbag could be deployed; and
wherein said tracking and predicting system integrates past predictions with current measurements utilizing probability weighted heuristics. - View Dependent Claims (16, 17, 18, 19)
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20. An image processing system comprising:
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a sensor for capturing an image of a seat area;
a segmentation subsystem for extracting a segmented image of an occupant from an ambient image of a seat area;
an ellipse fitting subsystem which fits an upper ellipse to the upper torso of an occupant, a lower ellipse to the bottom portion of an occupant, and a centroid in said upper ellipse;
a tracking and predicting system, further comprising a shape tracker and predictor and a motion tracker and predictor; and
an at-risk-zone intrusion indicator, wherein said at-risk-zone intrusion indicator determines which point on the upper ellipse is closest to the airbag, and wherein said at-risk-zone intrusion indicator informs the airbag controller whether the occupant will be in a position within a predefined danger zone by the time the airbag is deployed;
wherein said shape tracker and predictor tracks and predicts predefined shape characteristics using a Kalman filter equation for each predefined shape state, and wherein said shape tracker and predictor weighs each Kalman filter using the predefined probabilities associated with said shape states, to generate an overall prediction for each said shape characteristic; and
wherein said motion tracker and predictor predicts predefined motion characteristics using a Kalman filer equation for each predefined motion mode and weighs the predefined probabilities associated with said motion modes, to generate an overall prediction for each said motion characteristic.
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21. A method for predicting the movement of an image, comprising the steps of:
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applying a plurality of mathematical heuristics to a plurality of image characteristics to incorporate past measurements and past predictions into an updated overall prediction of said plurality of image characteristics, wherein the image is in one of a plurality of predefined modes and states; and
using said updated characteristic predictions to determine whether the image will enter an ARZ.
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Specification