Vehicle occupant weight classification system

• US 8,016,319 B2
• Filed: 07/30/2008
• Issued: 09/13/2011
• Est. Priority Date: 07/12/2000
• Status: Expired due to Fees

• Current Assignee: Continental Automotive GmbH (Continental AG)
• Original Assignee: Siemens AG
• Inventors: Schicker, Gerald, Knueppel, Andreas, Winkler, Gerd
• Primary Examiner(s): Boehler; Anne Marie M
• Assistant Examiner(s): Stabley; Michael R
• Application Number:
  US12/182,197
• Publication Number:
  US 20080281493A1
• Time in Patent Office: 1,140 Days
• Field of Search: 280/735, 180/273, 701/45, 74/29, 744/22, 340/436, 340/438, 340/667
• US Class Current: 280/735
• CPC Class Codes: B60R 21/01516 using force or pressure sensing means
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: Y10T 74/18088 Rack and pinion type
• CPC Class Codes: Y10T 74/1967 Rack and pinion

Abstract

A vehicle occupant classification system categorizes vehicle occupants into various classes such as adult, child, infant, etc. to provide variable control for a vehicle restraint system such as an airbag. The classification system utilizes sensors that are installed in various locations in the vehicle. The sensors are used to generate a three-dimensional profile for the vehicle occupant. Various factors can affect the accuracy of this three-dimensional profile. Fuzzy logic is used to reduce some of the inaccuracies by providing multiple decision levels for various stages of the classification. Inaccuracies are also caused by sensors shifting within the system from their original position. This condition creates offset and the system evaluates this offset and generates a correction factor to provide a more accurate three-dimensional profile. Electrically erasable programmable read-only memory is used to reduce complications and inaccuracies associated with seat occupant weight sensors that have mounting configurations that vary depending upon the vehicle.

Claims

1. A method for determining vehicle occupant characteristics to classify vehicle occupants comprising the steps of:

(a) establishing a first position for at least one occupant sensor;

(b) recognizing a second position that is different than the first position wherein the first position comprises an original sensor position and the second position comprises a subsequent sensor position that is offset from the original sensor position;

(c) using an information factor to determine a difference between the first and second positions by using a measuring function to evaluate the offset; and

(d) generating a correction factor to correct for the offset.

2. The method according to claim 1 including generating a first plurality of occupant signals from the at least one occupant sensor, generating an actual position of a three-dimensional profile based on the first plurality of occupant signals to comprise the second position, and comparing the actual position to original sensor arrangement to determine an offset.

3. The method according to claim 1 wherein the at least one occupant sensor comprises at least a plurality of weight sensors, and including installing the weight sensors in an initial sensor arrangement to define the first position, generating a first plurality of occupant signals from the weight sensors, and generating a three-dimensional profile based on the first plurality of occupant signals.

4. The method according to claim 1 wherein the at least one occupant sensor comprises a plurality of occupant sensors, and including generating a plurality of occupant measurement signals and generating a three-dimensional profile for occupant classification based on the plurality of occupant measurement signals.

5. A method for determining vehicle occupant characteristics to classify vehicle occupants comprising the steps of:

(a) establishing a first position for at least one occupant sensor;

(b) recognizing a second position that is different than the first position;

(c) using an information factor to determine a difference between the first and second positions;

(d) generating a correction factor to correct for the difference; and

(e) defining an occupant position by positioning an occupant on a seat, generating a first plurality of occupant signals from the at least one occupant sensor, generating a three-dimensional profile based on the first plurality of occupant signals, and classifying the occupant based on the three-dimensional profile.

6. The method according to claim 5 including comparing the three-dimensional profile to the information factor and generating a corrected three-dimensional profile if the information factor varies from the three-dimensional profile by a pre-determined amount.

7. The method according to claim 6 including adjusting occupant classification in response to generating a second plurality of occupant signals that are different than the first plurality of occupant signals.

8. The method according to claim 7 including generating a second three-dimensional profile based on the second plurality of occupant signals, comparing the second three-dimensional profile to the information factor, and generating a corrected three-dimensional profile if the second three-dimensional profile varies from the information factor by the pre-determined amount.

9. A method for determining vehicle occupant characteristics to classify vehicle occupants comprising the steps of:

(a) establishing a first position for at least one occupant sensor wherein the at least one occupant sensor comprises a plurality of occupant sensors mounted within a vehicle for generating a plurality of occupant signals, and including generating at least one initial occupant sensor value with the first position being defined as an initial sensor position arrangement based on the occupant sensor value;

(b) recognizing a second position that is different than the first position;

(c) using an information factor to determine a difference between the first and second positions, including generating the information factor based on the initial occupant sensor value, generating a three-dimensional occupant profile based on the plurality of occupant signals, comparing the information factor to the three-dimensional occupant profile, identifying variance when a current occupant sensor value varies from the initial occupant sensor value, defining the second position as a shifted sensor position arrangement once variance is identified, and determining an amount of variance between the initial and shifted sensor position arrangements;

(d) generating a correction factor to correct for the difference by applying the correction factor to a current three-dimensional occupant profile to generate a corrected three-dimensional occupant profile; and

(e) classifying the seat occupant based on the corrected three-dimensional occupant profile.

10. A method for determining vehicle occupant characteristics to classify vehicle occupants comprising the steps of:

(a) establishing a first position for at least one occupant sensor wherein the first position comprises an original sensor arrangement, and including generating a first plurality of occupant signals from the at least one occupant sensor;

(b) recognizing a second position that is different than the first position, and including generating an actual position of a three-dimensional profile based on the first plurality of occupant signals to comprise the second position,

(c) using an information factor to determine a difference between the first and second positions, and including comparing the actual position to original sensor arrangement to determine an offset; and

(d) generating a correction factor to correct for the difference by using a measuring function to evaluate the offset and preparing the correction factor to correct the three-dimensional profile.

11. A method for determining vehicle occupant characteristics to classify vehicle occupants comprising the steps of:

(a) establishing a first position for at least one occupant sensor, generating a first plurality of occupant signals from the at least one occupant sensor, and generating a three-dimensional profile based on the first plurality of occupant signals to define the first position;

(b) recognizing a second position that is different than the first position;

(c) using an information factor to determine a difference between the first and second positions, providing the information factor with a predefined sensor arrangement that defines the second position, and using the information factor to determine a difference between the first and second positions to determine an offset; and

(d) generating a correction factor to correct for the difference.

12. The method according to claim 11 including using a measuring function to determine the amount offset and generating the correction factor to adjust the actual sensor position input values of the three-dimensional profile resulting in a corrected three-dimensional profile.

13. A method for determining vehicle occupant characteristics to classify vehicle occupants comprising the steps of:

(a) establishing a first position for at least one occupant sensor wherein the at least one occupant sensor comprises at least a plurality of weight sensors, and including installing the weight sensors in an initial sensor arrangement to define the first position, generating a first plurality of occupant signals from the weight sensors, and generating a three-dimensional profile based on the first plurality of occupant signals;

(b) recognizing a second position that is different than the first position;

(c) using an information factor to determine a difference between the first and second positions wherein the information factor includes a virtual sensor matrix having a predefined number of virtual weight sensor positions that are greater in number than a number of sensors in the initial sensor arrangement, and including dividing the weight sensors into a plurality of zones with each zone defined as covering a subset of virtual weight sensor positions, assigning each zone a reference cell for compiling weight signals from that zone to generate a weight zone signal, mapping each weight zone signal in the virtual sensor matrix such that all virtual weight sensor positions are filled forming a subsequent sensor arrangement to define the second position; and

(d) generating a correction factor to correct for the difference.

14. The method according to claim 13 including determining seat occupant weight from the virtual sensor matrix to generate a corrected three-dimensional profile.

15. A method for determining vehicle occupant characteristics to classify vehicle occupants comprising the steps of:

(a) establishing a first position for at least one occupant sensor wherein the at least one occupant sensor comprises a plurality of occupant sensors, and including generating a plurality of occupant measurement signals and generating a three-dimensional profile for occupant classification based on the plurality of occupant measurement signals;

(b) recognizing a second position that is different than the first position;

(c) using an information factor to determine a difference between the first and second positions, including comparing the information factor to the three-dimensional profile; and

(d) generating a correction factor to correct for the difference, including generating the correction factor if the three-dimensional profile varies from the information factor by a predetermined amount.

16. The method according to claim 15 wherein the information factor includes a known predefined original sensor arrangement and wherein the three-dimensional profile represents an actual sensor position, and including comparing the actual sensor position to the original sensor arrangement to determine an offset, generating the correction factor to adjust for the offset, using the correction factor to generate a corrected three-dimensional profile, and classifying the occupant based on the corrected three-dimensional profile.

17. The method according to claim 16 including fuzzy logic to generate the three-dimensional profile, the corrected three-dimensional profile, and occupant classification.