Method for controlling output of a classification algorithm

• US 7,009,502 B2
• Filed: 08/20/2003
• Issued: 03/07/2006
• Est. Priority Date: 08/26/2002
• Status: Active Grant

• Current Assignee: American Vehicular Sciences LLC (Acacia Research Corporation)
• Original Assignee: Automotive Technologies International Incorporated
• Inventors: Breed, David S., Chen, Tie-Qi, Piirainen, Ray
• Primary Examiner(s): SWARTHOUT, BRENT
• Application Number:
  US10/644,334
• Publication Number:
  US 20040045758A1
• Time in Patent Office: 930 Days
• Field of Search: 340/425.5, 340/438, 340/439, 340/436, 340/667, 340/666, 701/45, 280/735, 280/734, 180/271, 702/127
• US Class Current: 340/436
• CPC Class Codes: B60R 21/01516 using force or pressure sensing means
• CPC Class Codes: B60R 21/0153 using field detection presence sensors

Abstract

Method for controlling output of a classification algorithm which classifies an occupant of a seat including initially classifying the occupant and outputting the classification, subsequently periodically classifying the occupant and enabling a change in the classification of the occupant only upon obtaining evidence of a new classification which is greater than evidence of the current classification. The initial classification of the occupant may be conducted based on satisfaction of a condition such as the detection of closure of a door by, e.g., a sensor such as a door closure sensor, the detection of an empty seat by, e.g., an occupant presence sensor, weight sensor, electric field sensor, wave sensor, camera and the like, the detection of the switching on of the vehicle ignition by an appropriate sensor, motion or the absence of motion of the vehicle.

Claims

1. A method for controlling output of a classification algorithm which classifies an occupant of a seat in a vehicle, comprising:

initially classifying the occupant and outputting a current classification;

setting an initial threshold period of time relative to which changes in the current classification are made;

conducting multiple re-classifications of the occupant;

determining a substantially consecutive period of time that the occupant has been classified or re-classified as the current classification;

determining which of the consecutive period of time that the occupant has been classified or re-classified as the current re-classification or the initial threshold is less and setting whichever is less as a comparison threshold;

determining a substantially consecutive period of time that the occupant has been classified differently than the current classification; and

changing the current classification only when the consecutive period of time that the occupant has been classified differently than the current classification is greater than the comparison threshold.

2. The method of claim 1, wherein the determination of the consecutive period of time that the occupant has been classified or re-classified as the current classification is made after each re-classification of the occupant when the re-classification of the occupant is the same as the current classification.

3. The method of claim 1, further comprising conducting the initial classification of the occupant based on satisfaction of a condition.

4. The method of claim 1, further comprising conducting the initial classification of the occupant based on detection of closure of a door.

5. The method of claim 1, further comprising:

providing a sensor to detect closure of the door; and

conducting the initial classification of the occupant upon detection of closure of the door.

6. The method of claim 5, wherein the sensor is a door closure sensor.

7. The method of claim 1, further comprising:

detecting an empty seat; and

conducting the initial classification of the occupant upon detection of an object in the seat after detection of an empty seat.

8. The method of claim 1, further comprising:

providing a sensor to detect an empty seat; and

conducting the initial classification of the occupant upon detection of an object in the seat after detection of an empty seat by the sensor.

9. The method of claim 1, further comprising:

detecting switching on of the vehicle ignition; and

conducting the initial classification of the occupant upon detection of an object in the seat after detection of the switching on of the vehicle ignition.

10. The method of claim 1, further comprising:

providing a sensor to detect switching on of the vehicle ignition; and

conducting the initial classification of the occupant upon detection of an object in the seat after detection of the switching on of the vehicle ignition by the sensor.

11. The method of claim 1, wherein the classification of the occupant is performed using a trained neural network.

12. The method of claim 1, wherein the classification of the occupant is performed using a combination neural network.

13. The method of claim 1, wherein the classification of the occupant is performed using a modular neural network.

14. The method of claim 1, further comprising obtaining data from sensors such that the classification of the occupant is performed using the data obtained from the sensors.

15. The method of claim 14, wherein the sensors include at least two different types of sensors each type being selected from a group consisting of a weight sensor, a capacitance-based sensor, an electric field-based sensor, a radar or other electromagnetic wave-based sensor, a camera-based sensors including a 3D sensor, and an ultrasonic-based sensor.

16. The method of claim 1, further comprising obtaining data from at least one camera such that the classification of the occupant is performed using the data obtained from the at least one camera.

17. The method of claim 1, further comprising:

resetting the classification by classifying the occupant and outputting the classification upon detection of a condition selected from a group consisting of an empty seat, opening of a door, ignition of the vehicle, motion of the vehicle and absence of motion of the vehicle.

35. The method of claim 1, further comprising resetting the classification by classifying the occupant and outputting the classification, upon detection of a condition selected from a group consisting of motion of the vehicle and absence of motion of the vehicle.

18. A method for controlling output of a classification algorithm which classifies an occupant of a seat, comprising:

initially classifying the occupant and outputting a current classification;

conducting multiple re-classifications of the occupant;

determining a substantially consecutive period of time that the occupant has been classified or re-classified as the current classification;

setting a threshold relative to which changes in the current classification can be made equal to the consecutive period of time that the occupant has been classified or re-classified as the current classification;

determining a substantially consecutive period of time that the occupant has been classified differently than the current classification;

changing the current classification of the occupant depending on analysis of the consecutive period of time that the occupant has been classified differently than the current classification relative to the threshold; and

adjusting the threshold as a function of the consecutive period of time that the occupant has been classified differently than the current classification.

19. The method of claim 18, further comprising conducting the initial classification of the occupant based on satisfaction of a condition.

20. The method of claim 18, further comprising:

detecting an empty seat; and

conducting the initial classification of the occupant upon detection of an object in the seat after detection of an empty seat.

21. The method of claim 18, further comprising:

detecting switching on of the vehicle ignition; and

conducting the initial classification of the occupant upon detection of an object in the seat after detection of the switching on of the vehicle ignition.

22. The method of claim 18, wherein the classification of the occupant is performed using a trained neural network, a combination neural network or a modular neural network.

23. The method of claim 18, further comprising obtaining data from sensors such that the classification of the occupant is performed using the data obtained from the sensors.

24. The method of claim 18, further comprising:

resetting the classification by classifying the occupant and outputting the classification upon detection of a condition selected from a group consisting of an empty seat, opening of a door, ignition of the vehicle, motion of the vehicle and absence of motion of the vehicle.

36. The method of claim 18, further comprising resetting the classification by classifying the occupant and outputting the classification upon detection of a condition selected from a group consisting of motion of the vehicle and absence of motion of the vehicle.

25. A method for controlling output of a classification algorithm which classifies an occupant of a seat in a vehicle, comprising:

initially classifying the occupant and outputting a current classification;

subsequently periodically re-classifying the occupant; and

enabling a change in the classification of the occupant upon satisfying pre-determined criteria; and

resetting the classification by classifying the occupant and outputting the classification, upon detection of a condition selected from a group consisting of motion of the vehicle and absence of motion of the vehicle,

the step of enabling a change in the classification of the occupant comprising;

determining a substantially consecutive period of time that the re-classification of the occupant is the same as the current classification;

detecting a change in classification and then determining a substantially consecutive period of time that the re-classification of the occupant is the changed classification; and

when the period of time in which the classification is the changed classification is greater than the period of time when the classification is the output classification, or greater than a predetermined time period, outputting the changed classification.

26. The method of claim 25, wherein the step of enabling a change in the classification of the occupant further comprises:

determining whether the period of time that the re-classification of the occupant is the same as the current classification is greater than a pre-determined time threshold and if so,

setting the period of time that the classification of the occupant is the same as the current classification to the pre-determined threshold.

27. The method of claim 25, further comprising conducting the initial classification of the occupant based on satisfaction of a condition.

28. The method of claim 25, further comprising:

detecting an empty seat; and

conducting the initial classification of the occupant upon detection of an object in the seat after detection of an empty seat.

29. The method of claim 25, further comprising:

detecting switching on of the vehicle ignition; and

conducting the initial classification of the occupant upon detection of an object in the seat after detection of the switching on of the vehicle ignition.

30. The method of claim 25, wherein the classification of the occupant is performed using a trained neural network, a combination neural network or a modular neural network.

31. The method of claim 25, further comprising obtaining data from sensors such that the classification of the occupant is performed using the data obtained from the sensors.

32. The method of claim 28, further comprising detecting the empty seat by means of a weight sensor arranged in connection with the seat.

33. The method of claim 28, further comprising detecting the empty seat by means of an electric field sensor.

34. The method of claim 28, further comprising detecting the empty seat by means of a wave-receiving sensor.

37. A method for controlling output of a classification algorithm which classifies an occupant of a seat in a vehicle, comprising:

initially classifying the occupant and outputting a current classification;

setting a threshold period of time relative to which changes in the current classification are made;

conducting multiple re-classifications of the occupant to obtain a tentative re-classification each time;

after each re-classification in which the tentative re-classification of the occupant is the same as the current classification,

determining a substantially consecutive period of time that the occupant has been classified or re-classified as the current classification; and

after each re-classification in which the tentative re-classification of the occupant is not the same as the current classification,

determining a substantially consecutive period of time that the occupant has been classified as the tentative re-classification, and

changing the current classification to the tentative re-classification of the occupant only when the consecutive period of time that the occupant has been classified as the tentative re-classification is greater than the lesser of the consecutive period of time that the occupant has been classified as the current classification or the threshold.

38. The method of claim 37, wherein the determination of the consecutive period of time that the occupant has been classified or re-classified as the current classification is made after each re-classification of the occupant when the re-classification of the occupant is the same as the current classification.

39. The method of claim 37, further comprising conducting the initial classification of the occupant based on satisfaction of a condition.

40. The method of claim 37, wherein the classification of the occupant is performed using a trained neural network, a combination neural network or a modular neural network.

41. The method of claim 37, further comprising obtaining data from sensors such that the classification of the occupant is performed using the data obtained from the sensors.

42. The method of claim 41, wherein the sensors include at least two different types of sensors each type being selected from a group consisting of a weight sensor, a capacitance-based sensor, an electric field-based sensor, a radar or other electromagnetic wave-based sensor, a camera-based sensors including a 3D sensor, and an ultrasonic-based sensor.

43. The method of claim 37, further comprising obtaining data from at least one camera such that the classification of the occupant is performed using the data obtained from the at least one camera.

44. The method of claim 37, further comprising

resetting the classification by classifying the occupant and outputting the classification upon detection of a condition selected from a group consisting of an empty seat, opening of a door, ignition of the vehicle, motion of the vehicle and absence of motion of the vehicle.

45. A method for controlling output of a classification algorithm which classifies an occupant of a seat, comprising:

initially classifying the occupant and outputting a current classification;

conducting multiple re-classifications of the occupant to obtain a tentative re-classification each time;

after each re-classification in which the tentative re-classification of the occupant is the same as the current classification,

determining a substantially consecutive period of time that the occupant has been classified or re-classified as the current classification, and

setting a threshold relative to which changes in the current classification can be made equal to the consecutive period of time that the occupant has been classified or re-classified as the current classification;

after each re-classification in which the tentative re-classification of the occupant is not the same as the current classification,

determining a substantially consecutive period of time that the occupant has been classified as the tentative re-classification, and

changing the current classification of the occupant to the tentative re-classification depending on analysis of the consecutive period of time that the occupant has been classified as the tentative re-classification relative to the threshold; and

adjusting the threshold as a function of the consecutive period of time that the occupant has been classified as the tentative re-classification.

46. The method of claim 45, further comprising resetting the classification by classifying the occupant and outputting the classification upon detection of a condition selected from a group consisting of an empty seat, opening of a door, ignition of the vehicle, motion of the vehicle and absence of motion of the vehicle.

47. A method for controlling output of a classification algorithm which classifies an occupant of a seat in a vehicle, comprising:

initially classifying the occupant and outputting a current classification;

subsequently periodically re-classifying the occupant; and

enabling a change in the classification of the occupant upon satisfying pre-determined criteria; and

resetting the classification by classifying the occupant and outputting the classification, upon detection of a condition selected from a group consisting of an empty seat, opening of a door, ignition of the vehicle, motion of the vehicle and absence of motion of the vehicle,

the step of enabling a change in the classification of the occupant comprising;

determining a substantially consecutive period of time that the re-classification of the occupant is the same as the current classification;

detecting a change in classification and then determining a substantially consecutive period of time that the re-classification of the occupant is the changed classification; and

when the period of time in which the classification is the changed classification is greater than the period of time when the classification is the output classification, outputting the changed classification.