System for determining the occupancy state of a seat in a vehicle and controlling a component based thereon

• US 6,757,602 B2
• Filed: 09/03/2002
• Issued: 06/29/2004
• Est. Priority Date: 02/06/1997
• Status: Expired due to Fees

• Current Assignee: Automotive Technologies International Incorporated
• Original Assignee: Automotive Technologies International Incorporated
• Inventors: Morin, Jeffrey L., Kussul, Michael E., Xu, Kunhong, Breed, David S., Johnson, Wendell C., DuVall, Wilbur E., Chen, Tie-Qi
• Primary Examiner(s): ARTHUR JEANGLAUDE, GERTRUDE
• Application Number:
  US10/234,436
• Publication Number:
  US 20030036835A1
• Time in Patent Office: 665 Days
• Field of Search: 701/45, 701/46, 701/47, 180/268, 180/271, 180/272, 180/273, 280/735
• US Class Current: 701/45
• CPC Class Codes: B60R 21/01516 using force or pressure sensing means
• CPC Class Codes: B60R 21/01532 using electric or capacitive field sensors
• CPC Class Codes: B60R 21/01534 using electromagneticwaves, e.g. infrared
• CPC Class Codes: B60R 21/01536 using ultrasonic waves
• CPC Class Codes: B60R 21/0154 in combination with seat heating
• CPC Class Codes: B60R 21/01546 using belt buckle sensors
• CPC Class Codes: B60R 21/01548 sensing the amount of belt winded on retractor
• CPC Class Codes: B60R 21/01554 Seat position sensors
• CPC Class Codes: G01S 15/04 Systems determining presence of a target
• CPC Class Codes: G01S 15/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S15/46
• CPC Class Codes: G01S 15/88 Sonar systems specially adapted for specific applications seismic or acoustic prospecting or detecting G01V1/00
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

Abstract

Method for controlling an occupant protection device in a vehicle in which data is acquired from at least one sensor relating to an occupant in a seat to be protected by the occupant protection device, the type of occupant is classified based on the acquired data and when the occupant is classified as an empty seat or a rear-facing child seat, deployment of the occupant protection device is disabled or adjusted. Otherwise, the size of the occupant is classified based on the acquired data, the position of the occupant is determined by one of a plurality of algorithms selected based on the classified size of the occupant using the acquired data, each algorithm being applicable for a specific size of occupant. Deployment of the occupant protection device is disabled or adjusted when the determined position of the occupant is more likely to result in injury to the occupant if the occupant protection device were to deploy. The algorithms may be pattern recognition algorithms such as neural networks.

Claims

1. A method for controlling an occupant protection device in a vehicle, comprising the steps of:

acquiring data from at least one sensor relating to an occupant in a seat to be protected by the occupant protection device;

classifying the type of occupant based on the acquired data;

when the occupant is classified as an empty seat or a rear-facing child seat, disabling or adjusting deployment of the occupant protection device;

otherwise

classifying the size of the occupant based on the acquired data;

determining the position of the occupant by means of one of a plurality of algorithms selected based on the classified size of the occupant using the acquired data, each of the algorithms being applicable for a specific size of occupant, and

disabling or adjusting deployment of the occupant protection device when the determined position of the occupant is more likely to result in injury to the occupant if the occupant protection device were to deploy.

2. The method of claim 1, wherein the algorithms are pattern recognition algorithms.

3. The method of claim 1, wherein the algorithms are neural networks.

4. The method of claim 1, wherein the step of acquiring data from at least one sensor comprises the step of arranging a plurality of sensors arranged in the vehicle, each of the sensors providing data relating to the occupancy state of the seat.

5. The method of claim 1, further comprising the step of selecting the at least one sensor from a group consisting of a camera, an ultrasonic sensor, a capacitive sensor or other electric or magnetic field monitoring sensor, a weight or other morphological characteristic detecting sensor and a seat position sensor.

6. The method of claim 1, further comprising the step of selecting the at least one sensor from a group consisting of an electromagnetic wave sensor, an electric field sensor, a seat belt buckle sensor, a seatbelt payout sensor, an infrared sensor, an inductive sensor, a radar sensor, a weight distribution sensor, a reclining angle detecting sensor for detecting a tilt angle of the seat between a back portion of the seat and a seat portion of the seat, an accelerometer and a heartbeat sensor for sensing a heartbeat of the occupant.

7. The method of claim 1, wherein the classification of the type of occupant and the size of the occupant is performed by a combination neural network created from a plurality of data sets, each of the data sets representing a different occupancy state of the seat and being formed from data from the at least one sensor while the seat is in that occupancy state.

8. The method of claim 1, further comprising the step of providing a previous determination of the position of the occupant to the algorithm for determining a current position of the occupant.

9. The method of claim 1, wherein the data from the at least one sensor is acquired periodically.

10. The method of claim 1, wherein adjustment of deployment of the occupant protection device when the occupant is classified as an empty seat or a rear-facing child seat comprises a depowered deployment, an oriented deployment or a late deployment.

11. The method of claim 1, further comprising the steps of:

determining whether the acquired data is compatible with data for classification of the type or size of the occupant; and

when the acquired data is not compatible with the data for classification of the type or size of the occupant, rejecting the acquired data and acquiring new data.

56. The method of claim 1, wherein the identification of the occupant, the determination of the orientation of the child seat, the determination of the position of the child seat and the determination of the at least one of the size and position of the occupant when other than a child seat is performed by a combination neural network created from a plurality of data sets, each of the data sets representing a different occupancy state of the seat and being formed from data from the at least one sensor while the seat is in that occupancy state.

12. A method for controlling a component in a vehicle, comprising the steps of:

acquiring data from at least one sensor relating to an occupant of a seat interacting with or using the component,

determining an occupancy state of the seat based on the acquired data;

periodically acquiring new data from the at least one sensor;

for each time new data is acquired, determining the occupancy state of the seat based on the acquired new data and the determined occupancy state from a preceding time; and

controlling the component based on the determined occupancy state of the seat.

13. The method of claim 12, wherein the step of acquiring data from at least one sensor comprises the step of arranging a plurality of sensors arranged in the vehicle, each of the sensors providing data relating to the occupancy state of the seat.

14. The method of claim 12, further comprising the step of selecting the at least one sensor from a group consisting of a camera, an ultrasonic sensor, a capacitive sensor or other electric or magnetic field monitoring sensor, a weight or other morphological characteristic detecting sensor and a seat position sensor.

15. The method of claim 12, further comprising the step of selecting the at least one sensor from a group consisting of an electromagnetic wave sensor, an electric field sensor, a seat belt buckle sensor, a seatbelt payout sensor, an infrared sensor, an inductive sensor, a radar sensor, a weight distribution sensor, a reclining angle detecting sensor for detecting a tilt angle of the seat between a back portion of the seat and a seat portion of the seat, an accelerometer and a heartbeat sensor for sensing a heartbeat of the occupant.

16. The method of claim 12, wherein the step of determining the occupancy state of the sate is performed using at least one pattern recognition algorithm.

17. The method of claim 12, wherein the step of determining the occupancy state of the sate is performed using a combination neural network.

18. A method for controlling a component in a vehicle, comprising the steps of:

acquiring data from at least one sensor relating to an occupant of a seat interacting with or using the component;

identifying the occupant based on the acquired data;

determining the position of the occupant based on the acquired data;

controlling the component based on at least one of the identification of the occupant and the determined position of the occupant;

periodically acquiring new data from the at least one sensor; and

for each time new data is acquired, identifying the occupant based on the acquired new data and an identification from a preceding time and determining the position of the occupant based on the acquired new data and then controlling the component based on at least one of the identification of the occupant and the determined position of the occupant.

19. The method of claim 18, wherein the step of determining the position of the occupant based on the acquired new data comprises the step of considering a determination of the position of the occupant from the preceding time.

20. The method of claim 18, wherein the step of acquiring data from at least one sensor comprises the step of arranging a plurality of sensors in the vehicle, each of the sensors providing data relating to the occupancy state of the seat.

21. The method of claim 20, wherein the step of identifying the occupant based on the acquired data comprises the step of using data from a first subset of the plurality of sensors and the step of determining the position of the occupant based on the acquired data comprises the step of using data from a second subset of the plurality of sensors different than the first subset.

22. The method of claim 18, further comprising the step of selecting the at least one sensor from a group consisting of a camera, an ultrasonic sensor, a capacitive sensor or other electric or magnetic field monitoring sensor, a weight or other morphological characteristic detecting sensor and a seat position sensor.

23. The method of claim 18, further comprising the step of selecting the at least one sensor from a group consisting of an electric or magnetic wave sensor, an electric field sensor, a seat belt buckle sensor, a seatbelt payout sensor, an infrared sensor, an inductive sensor, a radar sensor, a weight distribution sensor, a reclining angle detecting sensor for detecting a tilt angle of the seat between a back portion of the seat and a seat portion of the seat, an accelerometer and a heartbeat sensor for sensing a heartbeat of the occupant.

24. The method of claim 18, wherein the steps of identifying the occupant based on the acquired data and determining the position of the occupant based on the acquired data are performed using pattern recognition algorithms.

25. The method of claim 18, wherein the steps of identifying the occupant based on the acquired data and determining the position of the occupant based on the acquired data are performed using a combination neural network.

26. The method of claim 18, wherein the step of determining the position of the occupant comprises the step of inputting the acquired data into one of a plurality of algorithms selected based on the identification of the occupant, each of the algorithms being applicable for a specific identification of the occupant.

27. The method of claim 26, wherein the algorithms are pattern recognition algorithms.

28. The method of claim 26, wherein the algorithms are neural networks.

29. The method of claim 18, further comprising the step of classifying the size of the occupant, the step of determining the position of the occupant comprising the step of inputting the acquired data into one of a plurality of algorithms selected based on the size of the occupant, each of the algorithms being applicable for a specific size of occupant.

30. The method of claim 29, wherein the algorithms are pattern recognition algorithms.

31. The method of claim 29, wherein the algorithms are neural networks.

32. A method for controlling a component in a vehicle, comprising the steps of:

acquiring data from at least one sensor relating to an occupant of a seat interacting with or using the component;

identifying an occupant based on the acquired data;

determining the position of the occupant based on the acquired data;

controlling the component based on at least one of the identification of the occupant and the determined position of the occupant;

periodically acquiring new data from the at least one sensor; and

for each time new data is acquired, identifying an occupant based on the acquired new data and determining the position of the occupant based on the acquired new data and a determination of the position of the occupant from a preceding time and then controlling the component based on at least one of the identification of the occupant and the determined position of the occupant.

33. The method of claim 32, wherein the step of identifying the occupant based on the acquired new data comprises the step of considering an identification of the occupant from the preceding time.

34. The method of claim 32, wherein the step of acquiring data from at least one sensor comprises the step of arranging a plurality of sensors arranged in the vehicle, each of the sensors providing data relating to the occupancy state of the seat.

35. The method of claim 34, wherein the step of identifying the occupant based on the acquired data comprises the step of using data from a first subset of the plurality of sensors and the step of determining the position of the occupant based on the acquired data comprises the step of using data from a second subset of the plurality of sensors different than the first subset.

36. The method of claim 32, further comprising the step of selecting the at least one sensor from a group consisting of a camera, an ultrasonic sensor, a capacitive sensor or other electric or magnetic field monitoring sensor, a weight or other morphological characteristic detecting sensor and a seat position sensor.

37. The method of claim 32, further comprising the step of selecting the at least one sensor from a group consisting of an electric or magnetic wave sensor, an electric field sensor, a seat belt buckle sensor, a seatbelt payout sensor, an infrared sensor, an inductive sensor, a radar sensor, a weight distribution sensor, a reclining angle detecting sensor for detecting a tilt angle of the seat between a back portion of the seat and a seat portion of heartbeat of the occupant.

38. The method of claim 32, wherein the steps of identifying the occupant based on the acquired data and determining the position of the occupant based on the acquired data are performed using pattern recognition algorithms.

39. The method of claim 32, wherein the steps of identifying the occupant based on the acquired data and determining the position of the occupant based on the acquired data are performed using a combination neural network.

40. The method of claim 32, wherein the step of determining the position of the occupant comprises the step of inputting the acquired data into one of a plurality of algorithms selected based on the identification of the occupant, each of the algorithms being applicable for a specific identification of the occupant.

41. The method of claim 40, wherein the algorithms are pattern recognition algorithms.

42. The method of claim 40, wherein the algorithms are neural networks.

43. The method of claim 32, further comprising the step of classifying the size of the occupant, the step of determining the position of the occupant comprising the step of inputting the acquired data into one of a plurality of algorithms selected based on the size of the occupant, each of the algorithms being applicable for a specific size of occupant.

44. The method of claim 43, wherein the algorithms are pattern recognition algorithms.

45. The method of claim 43, wherein the algorithms are neural networks.

46. A method for controlling a component in a vehicle, comprising the steps of:

acquiring data from at least one sensor relating to an occupant of a seat interacting with or using the component;

identifying the occupant based on the acquired data;

when the occupant is identified as a child seat,

determining the orientation of the child seat based on the acquired data,

determining the position of the child seat by means of one of a plurality of algorithms selected based on the determined orientation of the child seat, each of the algorithms being applicable for a specific orientation of a child seat, and

controlling the component based on the determined position of the child seat; and

when the occupant is identified as other than a child seat,

determining at least one of the size and position of the occupant, and

controlling the component based on the at least one of the size and position of the occupant.

47. The method of claim 46, wherein the step of acquiring data from at least one sensor comprises the step of arranging a plurality of sensors arranged in the vehicle, each of the sensors providing data relating to the occupancy state of the seat.

48. The method of claim 47, wherein the step of identifying the occupant based on the acquired data comprises the step of using data from a first subset of the plurality of sensors and the step of determining the orientation of the child seat based on the acquired data comprises the step of using data from a second subset of the plurality of sensors different than the first subset.

49. The method of claim 46, wherein the identification of the occupant is performed by a pattern recognition algorithm.

50. The method of claim 46, wherein the determination of the orientation of the child seat is performed by a pattern recognition algorithm.

51. The method of claim 46, wherein the determination of the at least one of the size and position of the occupant when other than a child seat is performed by a pattern recognition algorithm.

52. The method of claim 46, wherein the algorithms are pattern recognition algorithms.

53. The method of claim 46, wherein the algorithms are neural networks.

54. The method of claim 46, further comprising the step of selecting the at least one sensor from a group consisting of a camera, an ultrasonic sensor, a capacitive sensor or other electric or magnetic field monitoring sensor, a weight or other morphological characteristic detecting sensor and a seat position sensor.

55. The method of claim 46, further comprising the step of selecting the at least one sensor from a group consisting of an electromagnetic wave sensor, an electric field sensor, a seat belt buckle sensor, a seatbelt payout sensor, an infrared sensor, an inductive sensor, a radar sensor, a weight distribution sensor, a reclining angle detecting sensor for detecting a tilt angle of the seat between a back portion of the seat and a seat portion of the seat, an accelerometer and a heartbeat sensor for sensing a heartbeat of the occupant.