WO2002004250A2 - Hardware independent mapping of multiple sensor configurations for classification of persons - Google Patents

Hardware independent mapping of multiple sensor configurations for classification of persons Download PDF

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Publication number
WO2002004250A2
WO2002004250A2 PCT/US2001/021358 US0121358W WO0204250A2 WO 2002004250 A2 WO2002004250 A2 WO 2002004250A2 US 0121358 W US0121358 W US 0121358W WO 0204250 A2 WO0204250 A2 WO 0204250A2
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WO
WIPO (PCT)
Prior art keywords
sensors
seat
virtual
sensor
predetermined number
Prior art date
Application number
PCT/US2001/021358
Other languages
French (fr)
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WO2002004250A3 (en
Inventor
Gerd Winkler
Original Assignee
Siemens Automotive Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Automotive Corporation filed Critical Siemens Automotive Corporation
Priority to DE60101853T priority Critical patent/DE60101853T2/en
Priority to JP2002508933A priority patent/JP3803637B2/en
Priority to EP01950917A priority patent/EP1299268B1/en
Publication of WO2002004250A2 publication Critical patent/WO2002004250A2/en
Publication of WO2002004250A3 publication Critical patent/WO2002004250A3/en

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Classifications

    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60RVEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R21/01Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
    • B60R21/015Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting the presence or position of passengers, passenger seats or child seats, and the related safety parameters therefor, e.g. speed or timing of airbag inflation in relation to occupant position or seat belt use
    • B60R21/01512Passenger detection systems
    • B60R21/01516Passenger detection systems using force or pressure sensing means
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

Definitions

  • This invention relates to a method and apparatus for classifying vehicle occupants utilizing common hardware for multiple seat sensor configurations. Specifically, physical sensors are mapped into a virtual matrix from which an occupant classification is determined.
  • Most vehicles include airbags and seatbelt restraint systems that work together to protect the driver and passengers from experiencing serious injuries due to highspeed collisions. It is important to control the deployment force of the airbags based on the size of the driver or the passenger. When an adult is seated on the vehicle seat, the airbag should be deployed in a normal manner. If there is a small child sitting on the vehicle seat, then the airbag should not be deployed or should be deployed at a significantly lower deployment force.
  • One way to control the airbag deployment is to monitor the weight of the seat occupant. The weight information can be used to classify seat occupants into various groups, e.g., adult, child, infant seat, etc., to ultimately control the deployment force of the airbag.
  • One type of system uses a plurality of sensors mounted within the seat bottom cushion. Information from the sensors is sent to system hardware, which utilizes software to combine the output from the sensors to determine the weight of the seat occupant. Often, these sensors must be placed symmetrically within the seat cushion in order to be compatible with the system hardware and software. Sometimes, due to specific seat design or limited space within the seat cushion, symmetrical placement of the sensors is difficult to achieve. Another problem with current seat sensor configurations is that each different sensor configuration requires different system hardware and software to account for the variations in sensor placement. Thus, it is difficult to optimize sensor placement because of restrictions with regard to row and column placement of the sensors.
  • the subject invention includes a method and apparatus for classifying vehicle occupants utilizing common hardware for multiple seat sensor configurations.
  • Multiple seat sensors are mapped into a virtual matrix from which an occupant classification is determined.
  • the seat sensors are preferably mounted within a seat bottom cushion or the seat structure.
  • the sensors can be mounted in a symmetrical or non-symmetrical pattern.
  • the virtual matrix defines an optimal pattern having an optimal number of seat sensor positions.
  • the sensors are mounted in a first configuration having one physical sensor for each virtual seat sensor position of the optimal pattern.
  • One occupant weight signal from each sensor is mapped into one corresponding seat sensor position in the optimal pattern.
  • the difference between the number of virtual cell locations in the virtual matrix and the number of physical sensors mounted within the seat bottom define a remaining number of virtual cell positions. A value is assigned to each of the remaining virtual cell positions based on data from the surrounding physical sensors.
  • electrically erasable programmable read only memory is used to map the virtual matrix by determining values for each of the remaining number of virtual cell positions.
  • the EEPROM is preferably mounted on a printed circuit board that is common to all seat sensor configurations.
  • the subject invention provides a method and apparatus for classifying seat occupants that can be used for symmetrical and non-symmetrical sensor configurations and utilizes common hardware for each different seat sensor configurations.
  • Figure 1 is a schematic representation of a vehicle seat and airbag system incorporating the subject invention.
  • Figure 2 is a schematic view of one seat sensor mounting configuration incorporating the subject invention.
  • Figure 3A is a schematic view of an alternate embodiment of a seat sensor mounting configuration incorporating the subject invention.
  • Figure 3B is a schematic view of the sensor configuration of Figure 3A incorporating a virtual matrix.
  • Figure 4 is a schematic view of the control system incorporating the subject invention.
  • a vehicle includes a vehicle seat assembly, shown generally at 12 in Figure 1, and a restraint system including an airbag 14.
  • the seat assembly 12 is preferably a passenger seat and includes a seat back 16 and a seat bottom 18.
  • a vehicle occupant 20 exerts a force F against the seat bottom 18.
  • the vehicle occupant 20 can be an adult, child, or infant in a car seat.
  • the airbag system 14 deploys an airbag 24 under certain collision conditions.
  • the deployment force for the airbag 24, shown as deployed in dashed lines in Figure 1, varies depending upon the type of occupant that is seated on the seat 12. For and adult, the airbag 24 is deployed in a normal manner shown in Figure 1. If there is child or an infant in a car seat secured to the vehicle seat 12 then the airbag 24 should not be deployed or should be deployed at a significantly lower deployment force. Thus, it is important to be able to classify seat occupants in order to control the various restraint systems.
  • One way to classify occupants is to monitor and measure the weight force F exerted on the seat bottom 18.
  • Multiple seat sensors 26 are mounted within the seat bottom 18 for generating occupant weight signals 28 representing portions of the occupant weight exerted against each respective sensor 26. The signals 28 are transmitted to a central control unit 30 and the combined output from the sensors 26 is used to determine seat occupant weight. This process will be discussed in greater detail below.
  • the classification information can be used in a variety of ways.
  • the classification information can be used in a vehicle restraint system including an airbag system 14.
  • the classification information can be transmitted to an airbag control. If the classification indicates that an adult is in the seat 12 then the airbag 24 is deployed in a normal manner. If the classification indicates that a child or infant is the seat occupant then the airbag 24 will not be deployed or will be deployed at a significantly lower deployment force.
  • the seat sensors 26 can be mounted within the seat bottom 18 in any of various configurations.
  • the sensors 26 can be mounted in a symmetrical configuration, see Fig. 2, or a non-symmetrical pattern, see Fig. 4.
  • the sensors 26 are preferably mounted into the seat bottom 18 in a series of rows and columns. The number of rows and columns can vary, however, Figure 2 is exemplary of a fully equipped sensor configuration.
  • FIG 3A depicts an alternate sensor mounting configuration. Tins embodiment has one less row, indicated at 30, than the configuration shown in Figure 2. Reconfiguring the number of rows and/or columns is typically in response to customer requirements for a seat that includes an extra trench to define seat cushion sections. Or, for smaller seats, it may also be necessary to reduce the number of rows and columns.
  • a virtual matrix 40 is used to take the place of the missing row as shown in Figure 3B.
  • the virtual matrix 40 includes virtual cell locations 42 to accommodate the sensors 26 that have been removed from an ideal pattern.
  • the virtual cells 42 are assigned values based on data from the surrounding physical sensors 26.
  • the central control unit 30 can then utilize an algorithm that is common to all seat sensor configurations to determine the seat occupant weight. The occupant can then be classified and the airbag system 14 can control the airbag deployment force based on this classification.
  • the weight signals 28 from the physical sensors 26 are transmitted to a central control unit 30.
  • the central control unit 30 is preferably a printed circuit board (PCB) 44 that includes a connector 46 with a plurality of ports for connection to the various sensors 26.
  • the PCB 44 includes a central processor unit (CPU) 48 and electrically erasable programmable read-only memory (EEPROM) 50.
  • EEPROM is a type of programmable read-only memory that can be erased by exposing it to an electrical charge and retains its contents even when the power is turned off.
  • the CPU 48 and EEPROM 50 receive the weight signals 28, generate the virtual matrix 40, and map the signals 28 into the matrix 40.
  • the CPU 48 then generates an output signal 52 to the airbag assembly 14 to control airbag deployment based on the seat occupant weight.
  • the operation of PCBs and EEPROMs are well known and will not be discussed in further detail. Also, while PCBs and EEPROMs are preferred, other similar components known in the art can also be used.
  • the system operates in the following manner.
  • the sensors 26 are mounted within the seat bottom 18 and generate a plurality of weight signals 28 in response to a weight force F applied to the seat bottom 18.
  • the signals 28 are transmitted to the central control unit 30 where they are mapped into virtual cells 42 in the virtual matrix 40.
  • the output from the virtual cells 42 in the matrix 40 is combined and used to generate an output signal representing the seat occupant weight.
  • Each seat occupant can then be classified into one of a plurality of predetermined occupant weight classes.
  • the classification information can then be used to control any of various restraint systems.
  • the virtual matrix 40 is configured to define an optimal pattern having an optimal number of virtual cells representing the optimal or maximum number of seat sensor positions.
  • the virtual matrix 40 can be generated as a full matrix having a maximum number of seat sensor positions where each physical sensor 26 is mapped into a virtual cell or the matrix 40 can be generated to represent the "missing" physical sensors 26 that the control unit 30 expects to receive signals from.
  • the weight signals 28 from the physical sensors 28 are combined with the data generated for the virtual row 30 to determine the seat occupant weight.
  • each sensor signal 28 is mapped into the virtual matrix 40 as shown in Figure 4.
  • the physical seat sensors 26 can be mounted within the seat bottom 18 in any of various configurations including a symmetrical row/column configuration or a non-symmetrical pattern.
  • the sensors 26 can be installed within the seat bottom 18 in a pattern that includes one physical sensor 26 for each virtual seat sensor position or cell 42 of the optimal pattern.
  • the control unit 30 would then map one occupant weight signal 28 from each physical sensor into one virtual seat sensor cell 42 in the optimal pattern.
  • the physical sensors 26 can be installed in the seat bottom 18 in an alternate pattern that has fewer physical sensors 26 than virtual seat sensor cells in the virtual matrix 40.
  • One occupant weight signal 28 from each of the physical sensors 26 is mapped into a corresponding virtual seat sensor cell 42 in the optimal pattern to define a remaining number of virtual sensor positions.
  • a value for each of the remaimng virtual sensor positions is determined based on information supplied by surrounding sensors 26.
  • any number of physical sensors 26 can be mounted within a seat in any type of pattern.
  • the weight signals 28 generated by the sensors 26 are then mapped into the virtual matrix 40 and any remaining virtual cells 42 are assigned values based on information from surrounding sensors.
  • electrically erasable programmable read only memory EEPROM is to map the virtual matrix 40 by determining values for each of the remaining number of virtual cells 42 with information from the surrounding cells.
  • position tables can be stored within the EEPROM to be used in conjunction with occupant weight signals 28 from surrounding sensors 26 to detennine values for each of the remaining number of virtual cells 42.
  • This unique process allows common hardware and software to be used for any seat sensor configuration, which significantly reduces system cost. This means that the same PCB 44 with the same CPU 48 and EEPROM 50 can be used for each different seat sensor configuration.
  • the subject invention also provides a method and apparatus for classifying seat occupants that can be used for symmetrical and non- symmetrical sensor configurations.

Abstract

Sensors are mounted within a seat structure for measuring seat occupant weight. The sensors can be mounted in any one of various sensor configurations. So that common hardware can be used for each different sensor configuration, a virtual matrix is created and output from the sensors is mapped into the virtual matrix. The virtual matrix includes cell locations that do not have a corresponding sensor output; i.e. there are fewer physical cells (sensors) than virtual cell locations in the virtual matrix. A weight output signal from each sensor is mapped into the corresponding position in the virutal matrix and the remaining virtual cell locations have values assigned tothem based on data supplied by the surrounding physical cells. Seat occupant weight is determined based on output from the virtual matrix and the occupant is placed into one of the various occupant classifications. Deployment force of a restraint system is controlled based on the classification of the seat occupant.

Description

HARDWARE INDEPENDENT MAPPING OF MULTIPLE SENSOR CONFIGURATIONS FOR CLASSIFICATION OF PERSONS
BACKGROUND OF THE TNVFNTTON
Field of the Invention.
This invention relates to a method and apparatus for classifying vehicle occupants utilizing common hardware for multiple seat sensor configurations. Specifically, physical sensors are mapped into a virtual matrix from which an occupant classification is determined.
Related Art.
Most vehicles include airbags and seatbelt restraint systems that work together to protect the driver and passengers from experiencing serious injuries due to highspeed collisions. It is important to control the deployment force of the airbags based on the size of the driver or the passenger. When an adult is seated on the vehicle seat, the airbag should be deployed in a normal manner. If there is a small child sitting on the vehicle seat, then the airbag should not be deployed or should be deployed at a significantly lower deployment force. One way to control the airbag deployment is to monitor the weight of the seat occupant. The weight information can be used to classify seat occupants into various groups, e.g., adult, child, infant seat, etc., to ultimately control the deployment force of the airbag.
There are many different systems for measuring the weight of a seat occupant. One type of system uses a plurality of sensors mounted within the seat bottom cushion. Information from the sensors is sent to system hardware, which utilizes software to combine the output from the sensors to determine the weight of the seat occupant. Often, these sensors must be placed symmetrically within the seat cushion in order to be compatible with the system hardware and software. Sometimes, due to specific seat design or limited space within the seat cushion, symmetrical placement of the sensors is difficult to achieve. Another problem with current seat sensor configurations is that each different sensor configuration requires different system hardware and software to account for the variations in sensor placement. Thus, it is difficult to optimize sensor placement because of restrictions with regard to row and column placement of the sensors.
Thus, it is desirable to have a method and apparatus for classifying seat occupants that can utilize common hardware and software for different seat sensor configurations. The method and apparatus should also work with symmetrical as well as non-symmetrical seat configurations in addition to overcoming the above referenced deficiencies with prior art systems.
SUMMARY OF THE TNVFNTTON The subject invention includes a method and apparatus for classifying vehicle occupants utilizing common hardware for multiple seat sensor configurations.
Multiple seat sensors are mapped into a virtual matrix from which an occupant classification is determined.
The seat sensors are preferably mounted within a seat bottom cushion or the seat structure. The sensors can be mounted in a symmetrical or non-symmetrical pattern. The virtual matrix defines an optimal pattern having an optimal number of seat sensor positions. h a disclosed embodiment of this invention, the sensors are mounted in a first configuration having one physical sensor for each virtual seat sensor position of the optimal pattern. One occupant weight signal from each sensor is mapped into one corresponding seat sensor position in the optimal pattern. Typically, there are more virtual seat sensors positions in the virtual matrix than there are physical seat sensors mounted within the seat. The difference between the number of virtual cell locations in the virtual matrix and the number of physical sensors mounted within the seat bottom define a remaining number of virtual cell positions. A value is assigned to each of the remaining virtual cell positions based on data from the surrounding physical sensors.
In a preferred embodiment, electrically erasable programmable read only memory (EEPROM) is used to map the virtual matrix by determining values for each of the remaining number of virtual cell positions. The EEPROM is preferably mounted on a printed circuit board that is common to all seat sensor configurations. The subject invention provides a method and apparatus for classifying seat occupants that can be used for symmetrical and non-symmetrical sensor configurations and utilizes common hardware for each different seat sensor configurations. These and other features of the present invention can be best understood from the following specification and drawings, the following of which is a brief description.
BRTEF DFSCRTPTTON OF THE DRAWINGS
Figure 1 is a schematic representation of a vehicle seat and airbag system incorporating the subject invention. Figure 2 is a schematic view of one seat sensor mounting configuration incorporating the subject invention.
Figure 3A is a schematic view of an alternate embodiment of a seat sensor mounting configuration incorporating the subject invention.
Figure 3B is a schematic view of the sensor configuration of Figure 3A incorporating a virtual matrix.
Figure 4 is a schematic view of the control system incorporating the subject invention.
PET ATT ED DESGRTPTTON OF AN EXEMPLARY EMBODIMENT A vehicle includes a vehicle seat assembly, shown generally at 12 in Figure 1, and a restraint system including an airbag 14. The seat assembly 12 is preferably a passenger seat and includes a seat back 16 and a seat bottom 18. A vehicle occupant 20 exerts a force F against the seat bottom 18. The vehicle occupant 20 can be an adult, child, or infant in a car seat. The airbag system 14 deploys an airbag 24 under certain collision conditions.
The deployment force for the airbag 24, shown as deployed in dashed lines in Figure 1, varies depending upon the type of occupant that is seated on the seat 12. For and adult, the airbag 24 is deployed in a normal manner shown in Figure 1. If there is child or an infant in a car seat secured to the vehicle seat 12 then the airbag 24 should not be deployed or should be deployed at a significantly lower deployment force. Thus, it is important to be able to classify seat occupants in order to control the various restraint systems. One way to classify occupants is to monitor and measure the weight force F exerted on the seat bottom 18. Multiple seat sensors 26 are mounted within the seat bottom 18 for generating occupant weight signals 28 representing portions of the occupant weight exerted against each respective sensor 26. The signals 28 are transmitted to a central control unit 30 and the combined output from the sensors 26 is used to determine seat occupant weight. This process will be discussed in greater detail below.
Once seat occupant weight is determined, the occupant is classified into one of any of the various predetermined occupant classes, e.g., adult, child, infant, etc. The classification information can be used in a variety of ways. For example, the classification information can be used in a vehicle restraint system including an airbag system 14. The classification information can be transmitted to an airbag control. If the classification indicates that an adult is in the seat 12 then the airbag 24 is deployed in a normal manner. If the classification indicates that a child or infant is the seat occupant then the airbag 24 will not be deployed or will be deployed at a significantly lower deployment force.
The seat sensors 26 can be mounted within the seat bottom 18 in any of various configurations. The sensors 26 can be mounted in a symmetrical configuration, see Fig. 2, or a non-symmetrical pattern, see Fig. 4. As shown in Figure 2, the sensors 26 are preferably mounted into the seat bottom 18 in a series of rows and columns. The number of rows and columns can vary, however, Figure 2 is exemplary of a fully equipped sensor configuration.
Figure 3A depicts an alternate sensor mounting configuration. Tins embodiment has one less row, indicated at 30, than the configuration shown in Figure 2. Reconfiguring the number of rows and/or columns is typically in response to customer requirements for a seat that includes an extra trench to define seat cushion sections. Or, for smaller seats, it may also be necessary to reduce the number of rows and columns.
In order to utilize common hardware and software with different seat sensor configurations, a virtual matrix 40 is used to take the place of the missing row as shown in Figure 3B. The virtual matrix 40 includes virtual cell locations 42 to accommodate the sensors 26 that have been removed from an ideal pattern. The virtual cells 42 are assigned values based on data from the surrounding physical sensors 26. The central control unit 30 can then utilize an algorithm that is common to all seat sensor configurations to determine the seat occupant weight. The occupant can then be classified and the airbag system 14 can control the airbag deployment force based on this classification.
As discussed above, the weight signals 28 from the physical sensors 26 are transmitted to a central control unit 30. As shown in Figure 4, the central control unit 30 is preferably a printed circuit board (PCB) 44 that includes a connector 46 with a plurality of ports for connection to the various sensors 26. The PCB 44 includes a central processor unit (CPU) 48 and electrically erasable programmable read-only memory (EEPROM) 50. EEPROM is a type of programmable read-only memory that can be erased by exposing it to an electrical charge and retains its contents even when the power is turned off. The CPU 48 and EEPROM 50 receive the weight signals 28, generate the virtual matrix 40, and map the signals 28 into the matrix 40. The CPU 48 then generates an output signal 52 to the airbag assembly 14 to control airbag deployment based on the seat occupant weight. The operation of PCBs and EEPROMs are well known and will not be discussed in further detail. Also, while PCBs and EEPROMs are preferred, other similar components known in the art can also be used. The system operates in the following manner. The sensors 26 are mounted within the seat bottom 18 and generate a plurality of weight signals 28 in response to a weight force F applied to the seat bottom 18. The signals 28 are transmitted to the central control unit 30 where they are mapped into virtual cells 42 in the virtual matrix 40. The output from the virtual cells 42 in the matrix 40 is combined and used to generate an output signal representing the seat occupant weight. Each seat occupant can then be classified into one of a plurality of predetermined occupant weight classes. The classification information can then be used to control any of various restraint systems.
Preferably, the virtual matrix 40 is configured to define an optimal pattern having an optimal number of virtual cells representing the optimal or maximum number of seat sensor positions. The virtual matrix 40 can be generated as a full matrix having a maximum number of seat sensor positions where each physical sensor 26 is mapped into a virtual cell or the matrix 40 can be generated to represent the "missing" physical sensors 26 that the control unit 30 expects to receive signals from. In this second embodiment, shown in Figures 3A and 3B, the weight signals 28 from the physical sensors 28 are combined with the data generated for the virtual row 30 to determine the seat occupant weight.
In the preferred embodiment, each sensor signal 28 is mapped into the virtual matrix 40 as shown in Figure 4. As discussed above, the physical seat sensors 26 can be mounted within the seat bottom 18 in any of various configurations including a symmetrical row/column configuration or a non-symmetrical pattern. For example, in one configuration the sensors 26 can be installed within the seat bottom 18 in a pattern that includes one physical sensor 26 for each virtual seat sensor position or cell 42 of the optimal pattern. The control unit 30 would then map one occupant weight signal 28 from each physical sensor into one virtual seat sensor cell 42 in the optimal pattern. In the alternative, the physical sensors 26 can be installed in the seat bottom 18 in an alternate pattern that has fewer physical sensors 26 than virtual seat sensor cells in the virtual matrix 40. One occupant weight signal 28 from each of the physical sensors 26 is mapped into a corresponding virtual seat sensor cell 42 in the optimal pattern to define a remaining number of virtual sensor positions. A value for each of the remaimng virtual sensor positions is determined based on information supplied by surrounding sensors 26.
Thus, any number of physical sensors 26 can be mounted within a seat in any type of pattern. The weight signals 28 generated by the sensors 26 are then mapped into the virtual matrix 40 and any remaining virtual cells 42 are assigned values based on information from surrounding sensors. Preferably, electrically erasable programmable read only memory EEPROM is to map the virtual matrix 40 by determining values for each of the remaining number of virtual cells 42 with information from the surrounding cells. Optionally, position tables can be stored within the EEPROM to be used in conjunction with occupant weight signals 28 from surrounding sensors 26 to detennine values for each of the remaining number of virtual cells 42. This unique process allows common hardware and software to be used for any seat sensor configuration, which significantly reduces system cost. This means that the same PCB 44 with the same CPU 48 and EEPROM 50 can be used for each different seat sensor configuration. The subject invention also provides a method and apparatus for classifying seat occupants that can be used for symmetrical and non- symmetrical sensor configurations.
Although a preferred embodiment of this invention has been disclosed, it should be understood that a worker of ordinary skill in the art would recognize many modifications come within the scope of this invention. For that reason, the following claims should be studied to determine the true scope and content of this invention.

Claims

1. A method for classifying vehicle occupants by measuring seat occupant weight comprising the steps of:
(a) mounting a plurality of sensors wimin a seat structure;
(b) generating a plurality of occupant weight signals from the sensors in response to a weight force applied to the seat structure;
(c) mapping the weight signals into a virtual matrix; and
(d) determining seat occupant weight based on the virtual matrix.
2. The method according to claim 1 including (f) classifying each seat occupant into one of a plurality of predetermined occupant weight classes.-
3. The method according to claim 2 including (g) providing seat occupant weight classification to a restraint control.
4. The method according to claim 1 wherein step (a) further includes mounting the sensors in a non-symmetrical pattern.
5. The method according to claim 1 wherein step (a) further includes mounting the sensors in a symmetrical pattern.
6. The method according to claim 1 wherein step (c) further includes generating a virtual matrix to define an optimal pattern having an optimal number of seat sensor positions.
7. The method according to claim 6 wherein step (a) includes mounting the sensors into a first predetermined pattern to define a first seat sensor configuration wherein the first seat sensor configuration includes one sensor for each seat sensor position of the optimal pattern and step (c) further includes mapping one occupant weight signal from each sensor into one corresponding seat sensor position in the optimal pattern.
8. The method according to claim 6 wherein step (a) includes mounting a first number of sensors into a first predetermined pattern to define a first seat sensor configuration wherein the optimal pattern includes more seat sensor positions than the first number of sensors; step (c) further includes mapping one occupant weight signal from each of the first number of sensors into a corresponding seat sensor position in the optimal pattern to define a remaining number of virtual sensor positions, and determining a value for each of the remaining virtual sensor positions based on surrounding sensors from the first number of sensors.
9. The method according to claim 8 wherein step (a) includes mounting a second number of sensors into a second predetennined pattern to define a second seat sensor configuration that is different from the first seat sensor configuration wherein the optimal pattern includes more seat sensor positions than the second number of sensors; step (c) further includes mapping one occupant weight signal from each of the second number of sensors into a corresponding seat sensor position in the optimal pattern to define a remaining number of virtual sensor positions, and determining a value for each of the remaining virtual sensor positions based on surrounding sensors from the second number of sensors.
10. The method according to claim 1 including providing hardware for receiving the occupant weight signals, storing the virtual matrix, and mapping the weight signals into the virtual matrix.
11. The method according to claim 10 wherein step (a) includes mounting the sensors into one of multiple different seat sensor configurations and further including using common hardware for each different seat sensor configuration.
12. The method according to claim 10 wherein step (a) includes mounting the sensors into one of multiple different seat sensor configurations and further including using identical hardware for each different seat sensor configuration.
13. The method according to claim 6 wherein step (a) includes mounting a predetermined number of sensors within the seat structure wherein the predetermined number is less than the optimal number of seat sensor positions the difference defining a remaining number of virtual positions and wherein step (c) further includes using a electrically erasable programmable read only memory to map the virtual matrix by determining values for each of the remaimng number of virtual positions.
14. The method according to claim 13 including storing position tables within electrically erasable programmable read only memory to be used in conjunction with occupant weight signals from surrounding sensors to determine values for each of the remaining number of virtual positions.
15. A method for classifying vehicle occupants by measuring seat occupant weight comprising the steps of:
(a) mounting a plurality of sensors within a seat structure in a physical matrix having a first pattern with a first predetermined number of rows and a first predetermined number of columns;
(b) generating a plurality of occupant weight signals from the sensors in response to a weight force applied to the seat structure;
(c) generating a virtual matrix having a second pattern with a second predetermined number of rows and second predetermined number of columns wherein the second predetermined number of rows is greater than or equal to the first predetermined number of rows and/or the second predetermined number of columns is greater than or equal to the first predetermined number of columns;
(d) mapping the weight signals from the physical matrix into the virtual matrix by mapping one weight signal from each sensor location in the first predetermined number of rows and columns into a corresponding virtual location in the second predetermined number of rows and columns; and (e) combining data from each of the second predetermined number of rows and columns to determine seat occupant weight.
16. The method of claim 15 wherein the difference between the second predetermined number of rows and columns and the first predetermined number of rows and columns defines virtual sensor locations and step (d) further includes detenrώiing a value for each virtual sensor location by using data from the surrounding sensors in the first predetermined number of rows and columns.
17. The method of claim 16 wherein step (a) includes having a plurality of different first patterns to define a plurality of sensor configurations and including the step of using common hardware and software for every sensor configuration.
18. The method of claim 17 including using electrically erasable programmable read only memory for the mapping.
19. A system for determining seat occupant weight comprising: a plurality of sensors mounted within a seat structure for generating a plurality of occupant weight signals in response to a weight force applied to said seat structure; and a control unit electrically connected to said sensors for receiving said signals and mapping said signals into a virtual matrix to generate an output signal representing seat occupant weight.
20. A system according to claim 19 wherein said control unit generates said virtual matrix to define an optimal pattern having an optimal number of seat sensor positions and wherein said plurality of sensors are mounted within said seat structure to establish a first sensor configuration having a first predetermined number of sensors that is less than the optimal number of sensors to define a number of remaining virtual sensor positions, said control unit mapping one occupant weight signal into a corresponding seat sensor position in said virtual matrix and assigning a value to each of said remaining virtual sensor positions by utilizing weight signals from surrounding sensors.
21. A system according to claim 19 wherein said sensors are mounted within said seat structure in one of a plurality of seat sensor configurations and wherein said control unit includes hardware that is common to each of said seat sensor configurations.
22. A system according to claim 19 wherein said control unit includes electrically erasable programmable read only memory.
23. A system according to claim 19 wherein said control unit includes a printed circuit board having a plurality of connectors for attacliment to said sensors and a central processing unit for generating said virtual matrix and mapping said weight signals into said virtual matrix.
24. A system according to claim 19 including a restraint control wherein said output signal is classified into one of a plurality of predetermined occupant weight classes and transmitted to said restraint control.
PCT/US2001/021358 2000-07-12 2001-07-06 Hardware independent mapping of multiple sensor configurations for classification of persons WO2002004250A2 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
DE60101853T DE60101853T2 (en) 2000-07-12 2001-07-06 CIRCUIT-FREE TRANSFORMATION OF SENSOR CONFIGURATIONS FOR THE CLASSIFICATION OF PERSONS
JP2002508933A JP3803637B2 (en) 2000-07-12 2001-07-06 Mapping to configure multiple sensor configurations independent of hardware to classify occupants
EP01950917A EP1299268B1 (en) 2000-07-12 2001-07-06 Hardware independent mapping of multiple sensor configurations for classification of persons

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US21758100P 2000-07-12 2000-07-12
US60/217,581 2000-07-12
US26553301P 2001-01-31 2001-01-31
US60/265,533 2001-01-31
US28002101P 2001-03-30 2001-03-30
US60/280,021 2001-03-30

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WO2002004250A2 true WO2002004250A2 (en) 2002-01-17
WO2002004250A3 WO2002004250A3 (en) 2002-04-11

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US (5) US6735508B2 (en)
EP (1) EP1299268B1 (en)
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10239604A1 (en) * 2002-04-19 2003-11-13 Visual Analysis Ag Seat occupation detection method for vehicle passenger seat identifying seated person or object for corresponding control of airbag device
DE10239761B4 (en) * 2002-08-29 2007-10-25 Sartorius Ag Method and device for identifying the type of occupancy of a support surface
WO2011033360A1 (en) * 2009-09-15 2011-03-24 Toyota Jidosha Kabushiki Kaisha Vehicle seat occupancy sensor

Families Citing this family (35)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6578870B2 (en) * 2000-07-12 2003-06-17 Siemens Ag Vehicle occupant weight classification system
US7023775B2 (en) * 2001-03-22 2006-04-04 Matsushita Electric Industrial Co., Ltd. Recording apparatus and method, and reproduction apparatus and method for recording data to or reproducing data from a write once type information recording medium, and write once type information recording medium
JP3570629B2 (en) * 2002-02-20 2004-09-29 株式会社デンソー Occupant determination device using load sensor
JP4009155B2 (en) * 2002-07-22 2007-11-14 アイシン精機株式会社 Crew determination device
US6918612B2 (en) * 2003-03-07 2005-07-19 Autoliv Asp, Inc. Electronic seat occupant classification system
KR100513879B1 (en) 2003-08-08 2005-09-09 현대자동차주식회사 Method for Expansion Pressure Control of Assistant Seat Airbag
JP4007293B2 (en) * 2003-09-17 2007-11-14 アイシン精機株式会社 Seating detection device
DE10354602A1 (en) * 2003-11-21 2005-06-16 Robert Bosch Gmbh Connecting elements, methods for bus communication between a control device for controlling personal protection devices as a master and at least one connection element for weight measurement in a seat as a slave and bus system
US20050177290A1 (en) * 2004-02-11 2005-08-11 Farmer Michael E. System or method for classifying target information captured by a sensor
US7039514B2 (en) * 2004-03-10 2006-05-02 Delphi Technologies, Inc. Occupant classification method based on seated weight measurement
US7403845B2 (en) * 2004-06-07 2008-07-22 Delphi Technologies, Inc. Child seat monitoring system and method for determining a type of child seat
US7439866B2 (en) * 2004-08-09 2008-10-21 Delphi Technologies, Inc. Child restraint system comprising event data recorder, and method for providing data relating to installation or adjustment
US7422447B2 (en) * 2004-08-19 2008-09-09 Fci Americas Technology, Inc. Electrical connector with stepped housing
EP1791720B1 (en) * 2004-09-08 2014-04-30 Delphi Technologies, Inc. Apparatus and method for interconnecting a child seat and monitoring system
US7475903B2 (en) * 2005-04-08 2009-01-13 Robert Bosch Gmbh Weight based occupant classification system
US20060001545A1 (en) * 2005-05-04 2006-01-05 Mr. Brian Wolf Non-Intrusive Fall Protection Device, System and Method
US7443310B2 (en) * 2005-11-23 2008-10-28 Autoliv Asp, Inc. Remote sensor network system with redundancy
EP1981736A1 (en) * 2006-01-26 2008-10-22 TK Holdings, Inc. Occupant classification system
US20070198139A1 (en) * 2006-02-21 2007-08-23 Colm Boran Auto-address multi-sensor network
DE102006022539B4 (en) * 2006-05-15 2016-07-28 Robert Bosch Gmbh Control device, device for controlling personal protective equipment and method for controlling personal protective equipment
DE102006023466A1 (en) * 2006-05-18 2007-11-22 Siemens Ag Switch arrangement, sensor arrangement, method and apparatus for distinguishing a seat occupancy of a vehicle seat
EP1950099B1 (en) * 2007-01-26 2010-12-22 BAG Bizerba Automotive GmbH Sensor system and method for determining at least one of the weight and the position of a seat occupant
JP4339368B2 (en) * 2007-03-06 2009-10-07 カルソニックカンセイ株式会社 Vehicle occupant detection device
DE102007035924A1 (en) 2007-07-23 2009-01-29 Bag Bizerba Automotive Gmbh Sensor system and method for determining the weight and / or position of a seat occupant
DE102007036079A1 (en) * 2007-08-01 2009-02-05 GM Global Technology Operations, Inc., Detroit Method for operating a motor vehicle and control device
US8606465B2 (en) * 2008-11-12 2013-12-10 GM Global Technology Operations LLC Performance-based classification method and algorithm for drivers
KR101054779B1 (en) * 2008-12-02 2011-08-05 기아자동차주식회사 Passenger Identification System of Vehicle Using Weight Sensor
CN103221257B (en) 2010-10-07 2017-12-12 佛吉亚汽车座椅有限责任公司 For improving the acquisition, analysis and system, method and the part using occupant's specification of armchair structure and environment configurations
US9366588B2 (en) * 2013-12-16 2016-06-14 Lifescan, Inc. Devices, systems and methods to determine area sensor
JP6501529B2 (en) * 2015-01-16 2019-04-17 Dmg森精機株式会社 Method of processing a work using a machine tool and machine tool
US9889809B2 (en) * 2015-03-06 2018-02-13 Ford Global Technologies, Llc Vehicle seat thermistor for classifying seat occupant type
JP6574213B2 (en) * 2017-03-08 2019-09-11 アイシン精機株式会社 Seating sensor
DE102018214731A1 (en) * 2018-08-30 2020-03-05 Ford Global Technologies, Llc Means of transport with a vehicle seat
DE102018218168A1 (en) * 2018-10-24 2020-04-30 Robert Bosch Gmbh Method and control device for controlling at least one occupant protection device for a vehicle in the event of a collision and system for occupant protection for a vehicle
CA3139868A1 (en) * 2019-05-09 2020-11-12 Magna Seating Inc. Systems and methods for occupant classification

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2744548A1 (en) * 1996-02-07 1997-08-08 Leteurtre Jean SYSTEM AND METHOD FOR DETECTING THE PRESENCE OF A PERSON SITUATED IN A HABITACLE, IN PARTICULAR A PASSENGER OF A MOTOR VEHICLE
WO1999038731A1 (en) * 1998-01-28 1999-08-05 I.E.E. International Electronics & Engineering S.A.R.L. Evaluation method for a seat occupancy sensor
DE19945645A1 (en) * 1998-09-25 2000-04-20 Honda Motor Co Ltd Passenger detection device in vehicle, especially car, has at least two image recording devices, and detection devices which detect occupation of seat based on measured distance to seat

Family Cites Families (46)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4621533A (en) * 1984-11-19 1986-11-11 Eaton Corporation Tactile load sensing transducer
US5232243A (en) 1991-04-09 1993-08-03 Trw Vehicle Safety Systems Inc. Occupant sensing apparatus
US5605348A (en) 1993-11-03 1997-02-25 Trw Vehicle Safety Systems Inc. Method and apparatus for sensing a rearward facing child seat
US5454591A (en) 1993-11-03 1995-10-03 Trw Vehicle Safety Systems Inc. Method and apparatus for sensing a rearward facing child restraining seat
US5413378A (en) 1993-12-02 1995-05-09 Trw Vehicle Safety Systems Inc. Method and apparatus for controlling an actuatable restraining device in response to discrete control zones
US5626359A (en) 1993-12-02 1997-05-06 Trw Vehicle Safety Systems, Inc. Method and apparatus for controlling an actuatable restraining device in response to discrete control zones
US6891111B1 (en) * 1994-02-04 2005-05-10 Siemens Vdo Automotive Corporation Signal processing in a vehicle classification system
US5482314A (en) * 1994-04-12 1996-01-09 Aerojet General Corporation Automotive occupant sensor system and method of operation by sensor fusion
ES2136229T3 (en) 1994-10-17 1999-11-16 Iee Sarl PROCEDURE AND INSTALLATION OF DETECTION OF CERTAIN PARAMETERS OF AN AUXILIARY CHAIR FOR CHILDREN IN VIEW OF THE OPERATION OF TWO AIRBAGS OF A VEHICLE.
US5670853A (en) 1994-12-06 1997-09-23 Trw Vehicle Safety Systems Inc. Method and apparatus for controlling vehicle occupant position
US5474327A (en) 1995-01-10 1995-12-12 Delco Electronics Corporation Vehicle occupant restraint with seat pressure sensor
US5570903A (en) 1995-02-21 1996-11-05 Echlin, Inc. Occupant and infant seat detection in a vehicle supplemental restraint system
US5732375A (en) 1995-12-01 1998-03-24 Delco Electronics Corp. Method of inhibiting or allowing airbag deployment
JP2909961B2 (en) 1996-05-29 1999-06-23 アイシン精機株式会社 Seating detection device
US5957995A (en) * 1996-06-17 1999-09-28 Trimble Navigation Radio navigation emulating GPS system
ES2165587T3 (en) * 1996-10-03 2002-03-16 Iee Sarl PROCEDURE AND DEVICE FOR DETERMINING VARIOUS PARAMETERS OF A PERSON SITTED IN A SEAT.
US6015163A (en) 1996-10-09 2000-01-18 Langford; Gordon B. System for measuring parameters related to automobile seat
US5785347A (en) 1996-10-21 1998-07-28 Siemens Automotive Corporation Occupant sensing and crash behavior system
US5991676A (en) * 1996-11-22 1999-11-23 Breed Automotive Technology, Inc. Seat occupant sensing system
JP3728711B2 (en) 1996-11-29 2005-12-21 アイシン精機株式会社 Seating detection device
US5986221A (en) * 1996-12-19 1999-11-16 Automotive Systems Laboratory, Inc. Membrane seat weight sensor
US5821633A (en) 1997-01-08 1998-10-13 Trustees Of Boston University Center of weight sensor
US6345839B1 (en) * 1997-01-13 2002-02-12 Furukawa Electronics Co., Ltd. Seat fitted with seating sensor, seating detector and air bag device
US5865463A (en) * 1997-02-15 1999-02-02 Breed Automotive Technology, Inc. Airbag deployment controller
US5810392A (en) * 1997-02-15 1998-09-22 Breed Automotive Technology, Inc. Seat occupant sensing system
US5971432A (en) * 1997-02-15 1999-10-26 Breed Automotive Technology, Inc. Seat occupant sensing system
US5999882A (en) * 1997-06-04 1999-12-07 Sterling Software, Inc. Method and system of providing weather information along a travel route
CA2241399A1 (en) 1997-06-23 1998-12-23 Daniel Dumont Method and apparatus for controlling an airbag
US6364352B1 (en) * 1997-07-09 2002-04-02 Peter Norton Seat occupant weight sensing system
US5906393A (en) 1997-09-16 1999-05-25 Trw Inc. Occupant restraint system and control method with variable sense, sample, and determination rates
WO1999024285A1 (en) 1997-11-12 1999-05-20 Siemens Automotive Corporation A method and system for determining weight and position of a vehicle seat occupant
US6039344A (en) 1998-01-09 2000-03-21 Trw Inc. Vehicle occupant weight sensor apparatus
US6070687A (en) * 1998-02-04 2000-06-06 Trw Inc. Vehicle occupant restraint device, system, and method having an anti-theft feature
US6158768A (en) * 1998-02-20 2000-12-12 Trw Vehicle Safety Systems Inc. /Trw Inc. Apparatus and method for discerning certain occupant characteristics using a plurality of capacitive sensors
US6094610A (en) * 1998-03-30 2000-07-25 Trw Vehicle Safety Systems Inc. Characterizing a proximately located occupant body portion with a sensor matrix
US6092838A (en) * 1998-04-06 2000-07-25 Walker; Robert R. System and method for determining the weight of a person in a seat in a vehicle
US6199572B1 (en) * 1998-07-24 2001-03-13 Negocios De Estela S.A. Collapsible shelter/tent with frame locking mechanism
US6040532A (en) * 1998-10-26 2000-03-21 Trw Inc. Vehicle occupant weight sensor
DE10005445C2 (en) * 1999-02-08 2002-08-14 Takata Corp Diagnostic method for a seat load measuring device
US6988413B1 (en) * 1999-02-24 2006-01-24 Siemens Vdo Automotive Corporation Method and apparatus for sensing seat occupant weight
US6764094B1 (en) * 1999-06-25 2004-07-20 Siemens Vdo Automotive Corporation Weight sensor assembly for determining seat occupant weight
US6161891A (en) * 1999-10-21 2000-12-19 Cts Corporation Vehicle seat weight sensor
US6282473B1 (en) * 1999-12-07 2001-08-28 Trw Vehicle Safety Systems Inc. System and method for controlling a vehicle occupant protection device
US6341252B1 (en) * 1999-12-21 2002-01-22 Trw Inc. Method and apparatus for controlling an actuatable occupant protection device
DE60112049T2 (en) * 2000-02-25 2006-03-16 Siemens Vdo Automotive Corp., Auburn Hills SIGNAL PROCESSING IN A VEHICLE WEIGHT CLASSIFICATION SYSTEM
JP4176651B2 (en) * 2004-02-13 2008-11-05 富士重工業株式会社 Load detection device

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2744548A1 (en) * 1996-02-07 1997-08-08 Leteurtre Jean SYSTEM AND METHOD FOR DETECTING THE PRESENCE OF A PERSON SITUATED IN A HABITACLE, IN PARTICULAR A PASSENGER OF A MOTOR VEHICLE
WO1999038731A1 (en) * 1998-01-28 1999-08-05 I.E.E. International Electronics & Engineering S.A.R.L. Evaluation method for a seat occupancy sensor
DE19945645A1 (en) * 1998-09-25 2000-04-20 Honda Motor Co Ltd Passenger detection device in vehicle, especially car, has at least two image recording devices, and detection devices which detect occupation of seat based on measured distance to seat

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
K. BILLEN: "Occupant Classification System for Smart Restraint Systems" SAE 1999-01-0761, January 1999 (1999-01), pages 33-38, XP002184965 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10239604A1 (en) * 2002-04-19 2003-11-13 Visual Analysis Ag Seat occupation detection method for vehicle passenger seat identifying seated person or object for corresponding control of airbag device
US7567184B2 (en) 2002-04-19 2009-07-28 Bayerische Motoren Werke Aktiengesellschaft Method for establishing the occupation of a vehicle seat
DE10239761B4 (en) * 2002-08-29 2007-10-25 Sartorius Ag Method and device for identifying the type of occupancy of a support surface
WO2011033360A1 (en) * 2009-09-15 2011-03-24 Toyota Jidosha Kabushiki Kaisha Vehicle seat occupancy sensor
CN102498018A (en) * 2009-09-15 2012-06-13 丰田自动车株式会社 Vehicle seat occupancy sensor

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