Image processing system for estimating the energy transfer of an occupant into an airbag

• US 6,577,936 B2
• Filed: 11/05/2001
• Issued: 06/10/2003
• Est. Priority Date: 07/10/2001
• Status: Expired due to Fees

• Current Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Original Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Inventors: Farmer, Michael Edward
• Primary Examiner(s): ARTHUR JEANGLAUDE, GERTRUDE
• Application Number:
  US10/006,564
• Publication Number:
  US 20030033066A1
• Time in Patent Office: 582 Days
• Field of Search: 701/45, 701/47, 701/28, 180/268, 180/271, 280/735, 382/103, 382/104, 382/106, 340/438
• US Class Current: 701/45
• CPC Class Codes: B60R 2021/0044 Chest
• CPC Class Codes: B60R 21/01538 for image processing, e.g. cameras or sensor arrays
• CPC Class Codes: B60R 21/01542 detecting passenger motion
• CPC Class Codes: B60R 21/01552 detecting position of specific human body parts, e.g. face, eyes or hands
• CPC Class Codes: B60R 21/01558 monitoring crash strength
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/215 Motion-based segmentation
• CPC Class Codes: G06T 7/254 involving subtraction of images
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

Abstract

The present invention relates in general to systems used to determine whether an airbag should be deployed at full or only partial strength. In particular, the present invention is an image processing system that utilizes real-time streaming video-images from a video camera or other sensor to determine the mass, velocity, and kinetic energy of the occupant at the time that the occupant comes into contact with the deploying airbag. By predicting the kinetic energy of the occupant at the time of impact, an airbag can be deployed at an appropriate strength corresponding to the kinetic energy of the occupant. The kinetic energy of the deploying back at the moment of impact should be equal to the kinetic energy of the occupant. The invention captures the volume of the occupant from an image, and uses volume to calculate the mass of the occupant. A Kalman filter is used with respect to all measurements to incorporate past predictions and measurements into the most recent estimates and predictions in order to eliminate the “noise” associated with any particular measurement. The system predicts the position and shape of the occupant at a faster rate than the rate at which the camera collects data.

Claims

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 readings, an airbag, and an airbag controller, said image processing system comprising:

a tracking and predicting subsystem, including a sensor reading and an occupant characteristic, said tracking and predicting subsystem generating said occupant characteristic from said sensor reading; and

an impact assessment subsystem, including a impact metric, said impact assessment subsystem generating said impact metric from said occupant characteristic.

2. An image processing system as recited in claim 1, wherein a kinetic energy prediction is said impact metric.

3. An image processing system as recited in claim 1, wherein said impact metric relates to a point in time when the occupant impacts into the deploying airbag.

4. An image processing system as recited in claim 1, wherein said impact metric is generated at a quicker rate than the rate at which the sensor generates said sensor readings.

5. An image processing system as recited in claim 1, further comprising an ellipse fitting subsystem and an ellipse, said ellipse fitting subsystem generating said ellipse to represent the occupant.

6. An image processing system as recited in claim 1, wherein said tracking and predicting subsystem is an iterative tracking and predicting subsystem.

7. An image processing system as recited in claim 6, said iterative tracking and predicting subsystem including a plurality of Kalman filters, wherein said Kalman filters generate said impact metric.

8. An image processing system as recited in claim 1, wherein said impact metric determines the deployment strength of the airbag.

9. An image processing system as recited in claim 1, said impact assessment subsystem including a seat belt status.

10. An image processing system as recited in claim 1, further including a plurality of occupant characteristics comprising:

a velocity; and

one said occupant characteristic selected from the group including

a mass;

a volume;

an area; and

an axis of a three-dimensional environment.

11. An image processing system as in claim 1, said tracking and predicting subsystem further including:

a plurality of occupant characteristics, comprising shape characteristics and motion characteristics;

a motion tracker and predictor for tracking and predicting said motion characteristics; and

a shape tracker and predictor for tracking and predicting said shape characteristics;

wherein said impact metric is based on at least one said motion characteristic and at least one said shape characteristic.

12. An image processing system as in claim 11, wherein said shape characteristics include:

a height of the occupant; and

a shape of the upper torso of the occupant.

13. An image processing system as in claim 11, wherein said motion characteristics include:

a distance between the occupant and the airbag; and

a forward tilt angle.

14. An image processing system as in claim 11, including a plurality of predefined shape states, wherein said shape tracker and predictor uses said plurality of predefined shape states.

15. An image processing system as in claim 14, wherein said predefined states comprise

a state of leaning left;

a state of leaning right; and

a state of sitting generally centered.

16. An image processing system as in claim 11, including a plurality of predefined motion modes, wherein said motion tracker and predictor utilizes said plurality of predefined motion modes.

17. An image processing system as in claim 16, wherein said predefined motion modes comprise:

a mode of crashing;

a mode of being stationary; and

a mode of being human.

18. An image classification system as in claim 11, wherein said shape tracker and predictor comprises:

an update shape predictor;

an update covariance and gain matrices generator;

an update shape estimator; and

a combined shape estimate generator.

19. An image classification system as in claim 11, wherein said motion tracker and predictor comprises:

an update motion predictor;

an update covariance and gain matrices generator;

an update motion estimator; and

a combined motion estimate generator.

20. An image processing system as in claim 11, wherein said shape tracker and predictor determines a sideways tilt angle of the occupant.

21. An image processing system for use with an airbag deployment system having a seat, an occupant in the seat, a sensor for capturing occupant images, an airbag, an airbag controller, said image processing system comprising:

a segmentation subsystem, including an ambient image and a segmented image, said segmentation subsystem generating said segmented image from said ambient image;

an ellipse fitting subsystem, including an ellipse, said ellipse fitting subsystem representing said ambient image with said ellipse;

a tracking and predicting subsystem, including a plurality of occupant characteristics, said tracking an predicting subsystem generating said plurality of occupant characteristics from said ellipse and

an impact assessment subsystem, including an impact metric, said impact assessment subsystem generating said impact metric from said plurality of occupant characteristics.

22. An image processing system as recited in claim 21, said tracking and predicting subsystem further including a plurality of past predictions, wherein said plurality of past predictions are incorporated into said plurality of occupant characteristics.

23. An image processing system as recited in claim 22, said tracking and predicting subsystem applying a plurality of Kalman filter to incorporate said plurality of past predictions into said plurality of occupant characteristics.

24. A method for determining airbag deployment strength, comprising the steps of:

applying a plurality of mathematical heuristics to a plurality of image characteristics to incorporate past measurements and past predictions into a plurality of updated occupant characteristic predictions, and

calculating an impact metric representing the magnitude of the impact between the occupant and the airbag from the updated occupant characteristic predictions.

25. A method for determining airbag deployment strength as recited in claim 24, wherein the plurality of mathematical heuristics are Kalman filters.

26. A method for determining airbag deployment strength as recited in claim 24, wherein the impact metric is the kinetic energy of the occupant.