METHOD AND APPARATUS FOR SENSING SEAT OCCUPANCY

• US 20030220766A1
• Filed: 05/16/2003
• Published: 11/27/2003
• Est. Priority Date: 04/17/2002
• Status: Active Grant

• Current Assignee: North American Rescue LLC (Henry Schein Incorporated)
• Original Assignee: Darrel Saunders, Lynn Johner, Petr Musilek
• Inventors: JOHNER, LYNN, SAUNDERS, DARREL, MUSILEK, PETR
• Granted Patent: US 7,026,946 B2
• Time in Patent Office: Days
• US Class Current: 702/173
• CPC Class Codes: B60N 2/002 Seats provided with an occupancy detection means mounted therein or thereon detection means responsive to presence or absence of children B60N2/266
• CPC Class Codes: B60R 21/01516 using force or pressure sensing means
• CPC Class Codes: B60R 21/01534 using electromagneticwaves, e.g. infrared
• CPC Class Codes: G01G 19/4142 for controlling activation of safety devices, e.g. airbag systems electrical circuits for triggering safety arrangements in case of vehicle accidents B60R21/015

Abstract

Method and apparatus for identifying and categorizing the weight and characteristics of the occupant currently occupying a vehicle seat. The method for identifying and categorizing the occupant or object involves measuring the deflection of the upper surface of the seat cushion at several points and therefore the weight distribution of the occupant. The system contains multiple weight sensors arrayed for detecting the distribution of the load causing the seat deflection. The system also includes a sensor to measure ambient temperature, preferably for temperature compensation due to the effects extreme temperatures may have on the compression properties of the seat cushion material and the weight sensors. A system processor interprets the data acquired by the sensors, and utilizes an algorithm and weight tables to simulate a neural network in providing an output a control signal indicative of the categorization of the occupant or object.

Claims

1. A vehicle seat occupancy detection system comprising:

a seat bun forming a portion of the seating cushion for an occupant'"'"'s seat, wherein said seat bun has a substantially horizontal upper surface portion, a lower surface portion and a plurality of cavities arrayed in a predetermined pattern;

a plurality of weight sensor devices mounted substantially in said array of cavities; and

a processor for receiving said sensor signals and interpreting the signals from said plurality of weight sensors to produce an output control signal corresponding with the weight and pattern of a load applied to the upper surface portion of said seat bun.

2. A vehicle seat occupancy detection system as in claim 1, wherein each weight sensor device has first and second relatively movable parts being aligned for relative movement with respect to each other in a path that is substantially perpendicular to said seat bun surfaces, said first part being mounted below said upper surface portion, said second part being spaced below said first part;

each said weight sensor device being operative to produce signals indicative of the distance between said first and second parts.

3. A vehicle seat occupancy detection system as in claim 2, wherein said first part comprises a magnetic field emitter and said second part includes a Hall-effect sensor.

4. A vehicle seat occupancy detection system as in claim 3, wherein each said weight sensor device includes a compressible housing that encloses first and second parts of said sensor device and said housing is adapted for mounting into a cavity.

5. A vehicle seat occupancy detection system as in claim 4, wherein said compressible housing is formed to limit the amount of relative movement between the first and second parts and prevent said first and second parts from coming into direct contact with each other.

6. The system as set forth in claim 1, wherein said processor comprises a general purpose microprocessor operating a program, the program comprising an algorithm for producing the control signal, the combination of the microprocessor and the program being operative to access a table of weights for use by said algorithm, said table of weights derived by a training system for the fixed algorithm, the combination of the microprocessor and the program further operative to input the sensor signals and the table of weights into said algorithm and to operate said algorithm and thereby produce said output control signal.

7. The system as set forth in claim 6, wherein said algorithm includes a process that simulates a predetermined neural network comprising:

an input layer of a plurality of input nodes, each input node adapted to receive the sensor signal from a corresponding weight sensor and to produce a signal for use by nodes in a subsequent layer of a plurality of hidden nodes;

at least one hidden layer of a plurality of hidden nodes, each hidden node adapted to receive and weight each signal from each input node in said input layer and to produce a signal for use by nodes in a subsequent layer of a plurality of output nodes; and

an output layer of a plurality of output nodes, each output node adapted to receive and weight each signal from each hidden node in the preceding hidden layer and to produce an output signal.

8. The system as set forth in claim 7, wherein said process compares said output signals from each output node, determines which output signal is the most dominant, and selects the control signal according to the most dominant output signal.

9. The system as set forth in claim 8, wherein said processor further comprises means for maintaining the control signal produced in response to an occupant occupying the seat in a steady state during momentary changes in the occupant'"'"'s occupancy of the seat.

10. A sensor for use in a system that measures weight or other forces applied in a first direction, comprising:

a first element configured to be mounted on a relatively immovable support element;

a second element configured to be movable in a predetermined path with respect to said first element, in response to said applied weight or other forces;

a spring element mounted between said first and second elements. to provide predetermined resistive forces to the applied weight or other forces;

wherein, said first element contains a central bore for accepting and guiding said second element during its relative movement with respect to said first element along said predetermined path;

said second element contains a central probe that fits within said central bore for relative movement therein along said predetermined path;

said central probe includes a permanent magnet;

said first element contains a magnetic field sensor that is electrically and physically connected to a flexible circuit layer below said first element and that is mounted within said central bore in a position that is an extension of, but beyond said predetermined path; and

said flexible circuit layer coextends with the surface of said relatively immovable support element to which said first element is physically connected.

11. A sensor as in claim 10, wherein said magnetic field sensor is a Hall-effect sensor.

12. A sensor as in claim 10, wherein said spring element is formed from a non-magnetic material that does not interfere with said magnetic field sensor and is mounted outside said central bore.

13. A plurality of weight sensors for use in a system that measures weight or other forces applied in a first direction, wherein said sensors are each mounted on a relatively immovable base substrate in a predetermined array, and wherein each sensor includes:

a first element configured to be mounted on said base substrate;

a second element configured to be movable in a predetermined path with respect to said first element, in response to said applied weight or other forces;

a spring element mounted between said first and second elements to provide predetermined resistive forces to the applied weight or other forces;

wherein, said first element contains a central bore for accepting and guiding said second element during its relative movement with respect to said first element along said predetermined path;

said second element contains a central probe that fits within said central bore for relative movement therein along said predetermined path;

said central probe includes a permanent magnet;

said first element contains a magnetic field sensor with power, ground and output signal terminals that are electrically and physically connected to a flexible circuit layer below said first element and is mounted within said central bore in a position that is an extension of, but beyond said predetermined path; and

said flexible circuit coextends with the surface of said relatively immovable base substrate to which said first element is physically connected; and

said flexible circuit provides common ground and common power connections to corresponding ground and power terminals, and a dedicated output connection to the output signal terminal of each of said magnetic field sensors.

14. A sensor as in claim 13, wherein said magnetic field sensor is a Hall-effect sensor.

15. A sensor as in claim 13, wherein said spring element is formed from a non-magnetic material that does not interfere with said magnetic field sensor and is mounted outside said central bore.

16. A method of detecting the classification of an occupant load on the seat cushion of a vehicle comprising the steps of:

providing a seat bun forming a portion of the seating cushion for an occupant'"'"'s seat, wherein said seat bun is provided with a substantially horizontal upper surface portion, a lower surface portion and a plurality of cavities arrayed in a predetermined pattern;

providing a plurality of weight sensor devices mounted substantially in said array of cavities, wherein each weight sensor device is provided to have first and second relatively movable parts being aligned for relative movement substantially perpendicular to said seat bun surfaces, said first part being mounted below said upper surface portion, said second part also being spaced below said first part;

each said weight sensor device being operative to produce signals indicative of the distance between said first and second parts; and

providing a processor for receiving said sensor signals and interpreting the signals from said plurality of weight sensors to produce an output control classification signal corresponding with the weight and pattern of the occupant load applied to the upper surface portion of said seat bun.

17. A method as in claim 16, wherein said weight sensor devices are provided to include Hall-effect sensors and magnetic field emitters.

18. A method as in claim 16, wherein said weight sensor devices are provided to have said first part include a magnetic field emitter and to have aid second part include a Hall-effect sensor.

19. A method as in claim 18, wherein said weight sensor devices are provided to have a compressible housing that encloses first and second parts of said sensor device and said housing is adapted for mounting into a cavity.

20. A method as in claim 19, wherein said weight sensor devices are provided to have said compressible housing formed so as to limit the amount of relative movement between the first and second parts and to prevent said first and second parts from coming in direct contact with each other.

21. A method as in claim 16 wherein said processor is provided as a general purpose microprocessor operating a program, wherein the program performs an algorithm for producing the output control classification signal, the combination of the microprocessor and the program being operative to access a table of weights for use by said algorithm, said table of weights being derived by a training system for the fixed algorithm, the combination of the microprocessor and the program being further operative to input the sensor signals and the table of weights into said algorithm and to perform said algorithm to thereby produce said output control classification signal.

22. A method as set forth in claim 21 wherein said algorithm further performs a process that simulates a predetermined neural network comprising:

the steps of inputting all sensor signals to an input layer containing a plurality of input nodes, wherein each input node is adapted and provided to receive a sensor signal from a corresponding weight sensor and to produce a signal for use by nodes in a subsequent layer of a plurality of hidden nodes;

transmitting all signals at the input nodes to at least one hidden layer containing a plurality of hidden nodes, wherein each hidden node is adapted to receive and weight each signal from each input node and to produce a corresponding hidden node signal for use by nodes in a subsequent layer of a plurality of output nodes; and

transmitting all hidden node signals to the output layer containing a plurality of output nodes, wherein each output node is adapted to receive and weight each signal from each hidden node in the preceding layer of hidden nodes and to produce an output signal.

23. A method as set forth in claim 22 wherein said processor performs steps that compare said output signals from each output node, determine which output signal is the most dominant, and select the output control classification according to the most dominant output signal.

24. A method of classifying a load present on a vehicle seat, comprising the steps of:

providing a seat bun having top and bottom surfaces, the seat bun further provided with a plurality of laterally-spaced apart cavities extending upwardly from the bottom surface part way into the seat bun towards the top surface;

providing a load sensor in each cavity for determining the compression of the seat bun due to a load being present on the top surface, each load sensor being provided as operative to produce a sensor signal representative of the compression of the seat bun at its respective location in the seat bun;

providing signal processing coupled to each load sensor for receiving and interpreting the sensor signals to determine the distribution pattern and weight of the load present on the top surface and for producing a classification signal indicative of the class of the load, for a vehicle subsystem;

producing sensor signals in response to a load being present on the top surface;

receiving and interpreting the sensor signals to determine the distribution pattern and weight of the load thereby determining the class of the load; and

producing the classification signal.