Patent Document

This Application is a continuation-in-part of U.S. Ser. No. 10/249,527 filed Apr. 16, 2003 entitled “Method and Apparatus for Sensing Seat Occupancy” and claims the benefit of U.S. Provisional Application No. 60/373,312, filed Apr. 17, 2002. 
    
    
     BACKGROUND OF THE INVENTION 
     This invention generally relates to the field of automatic occupancy sensing systems for use in vehicle seats. More specifically, it relates to methods and apparatus employed to produce data corresponding to the weight and the weight distribution or compression pattern of the seat occupant and to gather and interpret the data by a computerized system. 
     The automotive airbag was designed to provide protection to passengers during vehicle collisions. Traditionally, the passenger-side airbag has been permanently ready to deploy in case of a collision involving front or side impact. However concerns about the impact on children and small adults have led to developments that may allow the driver or passenger to disengage the airbag by way of an on/off toggle or key switch. Because of its nature, i.e. operator/manual control, there is a chance of operator error by forgetting or neglecting to actuate the switch to the setting appropriate to the type of person occupying the passenger seat. The US National Highway Transportation and Safety Administration NHTSA issued a Federal Motor Vehicle Safety Standard FMVSS-208, to combat the danger due to operator error and for other reasons. FMVSS-208 requires that 25% of all passenger vehicles produced in the United States, during and after 2004, have an automatic airbag deployment suppression system. The automatic airbag deployment suppression system must determine the mode of airbag deployment to be either fully enabled or fully suppressed based on the current occupant of the seat. By 2008, the automatic airbag deployment suppression system must also control the rate and percentage of airbag deployment depending on the current occupant of the passenger seat and be present in 100% of all new vehicles produced or sold in the United States. 
     Several patents cited with this application illustrate attempts by others to sense whether the occupant in the passenger seat is an adult above a certain weight or not and provide a deactivation signal to the air bag deployment control if not. Many of the prior patents show the use of multiple sensors in multiple locations to determine such things as whether the occupant is a human being, the location of the face and more elaborate determinations. Many systems found in the prior art are complex and expensive to fabricate, calibrate and maintain. 
     SUMMARY OF INVENTION 
     The present application addresses the aforementioned problems of determining the appropriate deploying of airbags during vehicle collisions and the aforementioned requirements of FMVSS-208 by providing a novel method and apparatus for automatically sensing occupancy in a vehicle seat. 
     The system of the present invention is capable of distinguishing between the different patterns created by different occupants and their various seating positions on the seat, such as weight distribution patterns. The system&#39;s preferred purpose, but not its sole purpose, is to read sensor signals, interpret the signals, and relay data via the system processor to other vehicle management systems. For instance, another vehicle management system, that is not part of this invention, will determine the mode of the passenger-side airbag deployment system based on measured characteristics of the current seat occupant made by the system of the present invention. 
     In the present invention, method and apparatus are provided for identifying and categorizing the weight and weight distribution characteristics, (e.g., distribution or compression pattern) of the occupant occupying a seat in a vehicle. The method and apparatus of the present invention is embodied in a system that identifies and categorizes the occupant load placed on the seating surface or cushion of a seat—commonly referred to in the seating industry as a “seat bun”. This is done, whether the occupant load is human or otherwise and returns information that is useful for the management of various vehicle sub-systems. 
     The method for identifying and categorizing the occupant comprises measuring the deflection of the upper surface of the seat bun at multiple points due to compression as caused by the occupant. In its simplest embodiment, a single sensor made up of a sensor/emitter pair e.g., a Hall-effect sensor can be used to measure the load weight. However, in order to include the ability to measure the weight distribution pattern, the system utilizes multiple sensor/emitter pairs for detecting this deflection. In one embodiment, a two dimensional array of deflection sensors are used to detect the change in the distance between the upper and lower sections or surfaces of the seat bun at multiple points when an occupant load is applied. The sensors are physically connected to a flat substrate beneath the seat cushion. The sensors are responsive to a weight load placed on the seat cushion as the distance between the emitters and the sensors varies due to load compression of the cushion against the substrate. The use of multiple sensors in a predetermined array causes sensors to provide signals that can be analyzed in the form of a three-dimensional topographical map indicative of the load and distribution pattern. A processor receives the sensor output signals, to determine the occupant&#39;s weight and its weight distribution pattern and to provide data useful in the control of other vehicle sub-systems. 
     A neural network or other predictive learning or training method may be used to generate tables of variable factors unique to the particular seat configuration and construction. The on-board system processor can then utilize the tables in applying its analysis algorithm to the sensor readings in order to simulate a neural network analysis and generate meaningful output data to the vehicle control sub-systems. 
     The invention may also include an ambient air temperature sensor to measure the temperature within the vehicle. The information from the temperature sensor is used to compensate for the effect that temperature variations may have on the response characteristics of the sensors and the compression characteristics of the seat cushion material. 
     It is, therefore, an object of the present invention to supply a vehicle sub-system with information that can be used to control the enablement or disablement of an airbag deployment sub-system for associated airbags. 
     It is another object of the present invention to supply a vehicle sub-system with information that can be used to control the airbag deployment sub-system for full deployment, no deployment, or to any predetermined percentage of deployment between the two extremes. 
     It is a further object of the present invention to determine occupant weight, which is useful information for controlling vehicle sub-systems including, but not limited to, brake biasing, suspension valving, or abandoned occupants warning. 
     It is yet another object of the present invention to determine seat status, that is, whether it is empty or occupied by a human or by non-human objects, which is useful information for controlling vehicle sub-systems including, but not limited to, seat belt indicators and related or ancillary warning systems. 
     Broadly stated, one aspect of the apparatus of the present invention includes an array of weight sensors mounted in a seat bun, and a processor. The seat bun forms a portion of the seating cushion for a vehicle occupant&#39;s seat and has a substantially horizontal upper surface portion and a lower surface portion. Each weight sensor has first and second parts aligned for relative movement along a path that is substantially perpendicular or transverse to the seat bun surface. The first part is mounted within the seat bun and spaced below the upper surface, while the second part is mounted so as to be spaced below the first part. Each weight sensor is operative to produce signals indicative of the distance between the first and second parts and the processor receives the sensor signals and interprets the signals to produce an output that indicates the presence of a properly classified occupant in the seat. 
     Further objects, features, and advantages of the invention will become apparent from a consideration of the following detailed description, when taken in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic diagram illustrating an embodiment of the present invention and the inter-relationships of the various components. 
         FIG. 2  is a cross-sectional view showing an embodiment of a weight sensor mounted to sense a compression load applied to a portion of a seat cushion. 
         FIG. 3  is a perspective view of an array of weight sensors, such as shown in  FIG. 2 , mounted on a substrate. 
         FIG. 4  is a perspective view of another array of weight sensors, such as shown in  FIG. 2 , mounted on a substrate. 
         FIG. 5  is a plan view of a flexible circuit layer onto which a sensor array, such as that shown in  FIG. 4 , is connected. 
         FIG. 6  is a plan view of a sensor connection portion of the flexible circuit layer shown in  FIG. 5 . 
         FIG. 7  is a plot of output voltage versus air gap distance for a Hall-effect sensor such as may be used in the present invention. 
         FIG. 8  is a flow chart of an initial power-up sequence that may be used to calibrate the “zero” load characteristics of the weight sensors employed in the present invention. 
         FIG. 9  is a flow chart showing the overall method of steps that may be employed to read and analyze the outputs of the weight sensors. 
         FIG. 10  is a flow chart showing the sub-process designated as  500  in  FIGS. 8 and 9  to gather data from the sensors. 
         FIG. 11  is a flow chart showing the sub-process designated as  600  in  FIGS. 8 and 9  to pre-filter the sensor data. 
         FIG. 12  is a flow chart showing the sub-process designated as  700  in  FIG. 9  to determine if “rezero” recalibration of the sensor data is required. 
         FIG. 13  is a block diagram of a neural network simulation. 
         FIG. 14  is a flow chart showing the sub-process designated as  800  in  FIG. 9  to compute the control signal in a manner that simulates a neural network. 
         FIG. 15  illustrates the three classes or airbag deployment that are currently mandated by FMVSS-208, based upon several occupant determinations. 
     
    
    
     DETAILED DESCRIPTION 
     In  FIG. 1 , an occupancy detection system  100  is shown as one embodiment of the present invention. System  100  includes a seat having an upper seat cushion or “seat bun”  104  that has an upper seating surface  105  and a lower surface  103 . The seat also has a seat back cushion  107 . An array of weight sensors  108  is contained within seat bun  104 . A flexible circuit layer  112  is used to provide electrical interconnection between the individual sensors  108  and an associated processor  114 . Flexible circuit layer  112  and sensors  108  are physically mounted on a substrate  113 . Substrate  113  is more rigid than flexible circuit layer  112  and provides resistive support for the sensors when compressed by loads applied to upper seating surface  105 . Substrate  113  and flexible circuit layer  112  are attached to lower surface  103  of seat bun  104 . The entire seat unit is fixedly attached to a seat pan and support structure  115  that is connected to the associated vehicle by a seat adjustment or mounting mechanism represented at  117 . 
     Each weight sensor  108  of the array contains a sensor/emitter pair that is made up of a magnetic field sensor, such as a Hall-effect sensor, and a magnetic field emitter, such as a permanent magnet. An embodiment of a weight sensor  108  is more particularly described below in the discussion of  FIG. 2 . 
     In  FIG. 1 , a human occupant  102  is represented as being seated on surface  105  of seat bun  104 . This causes compression loading on the seat and a corresponding output from each weight sensor  108 . Each weight sensor  108  transmits the data in the form of an output signal at a dc voltage level indicative of the amount of loading, via flexible circuit layer  112 , to a system processor  114 . 
     An ambient temperature sensor  110  may be provided and is shown as connected to system processor  114 . Temperature sensor  110  is used to ascertain the ambient temperature in which system  100  is operating for the purpose of temperature compensating the data provided by sensors  108  in non-standard or extreme ambient temperatures. Once the system processor  114  has analyzed the temperature-compensated sensor data and produced the desired outputs, an output signal  116  is sent or made available to one or more external sub-systems. For instance, for an output signal  116  having a “1” value, an airbag deployment control system can use the information to enable deployment of an associated airbag in the event of a collision. Conversely, if output  116  is “0” value, an airbag deployment control system can use the information to suppress deployment of or disable an associated airbag in the event of a collision. Other variations of the output  116 , i.e. output of “0.5”, could be used to provide 50% or some other fractional deployment power applied to an associated airbag.  FIG. 15  illustrates the current FMVSS-208 mandate. Future airbag deployment systems may utilize partial or graduated airbag deployment for some of the classifications or modes that are now mandated for no deployment, based upon the value of output signal  116 . 
     In  FIG. 2 , a cross-sectional view is provided of one embodiment of a single weight sensor  108 , as shown in  FIG. 1 . In this view, a cavity  109  is formed in seat bun  104  and extends upwards from lower surface  103  in a manner that provides for a significant thickness of the seat bun above the cavity. Cavity  109  is generally cylindrical in shape and has a longitudinal axis “P” aligned transverse or substantially perpendicular to upper surface  105  of seat bun  104 . Cavity  109  is configured and sized to allow for accommodating the mounting of weight sensor  108  into seat bun  104 . 
     Weight sensor  108  is shown as a unitary assembly of elements, which includes a first part comprising a plunger element  118 , a second part comprising a base support element  120 , and a third part comprising a spring element  111 . Plunger element  118  includes a magnet  126  and base support element  120  surrounds and includes a magnetic field sensor  122 . In this embodiment, sensor  122  is a ratio-metric or linear Hall-effect sensor. That is, as the magnet  126  moves towards the sensor  122 , it causes a stronger magnetic field flux to be sensed by the sensor  122  and the output voltage is reduced in value. Together, the first and second parts  118  and  120  form part of a compressible housing for the weight sensor  108 . Flexible circuit layer  112  provides electrical connection to sensor element  122  and is sandwiched between lower surface  103  and a substrate  113 . Base support element  120  is formed about a lower central bore  125  and an upper central bore  135 . Lower central bore  125  is generally cylindrical in shape, centrally aligned about axis P, and surrounds sensor  122 . Lower central bore  125  has a first predetermined diameter and upper central bore  135  has a second predetermined diameter. The second predetermined diameter is less than the first predetermined diameter. Plunger element  118  contains an upper head portion  134  and a probe portion  127 . Plunger element  118  is mounted on base support element  120  so that its probe portion  127  is movable along a linear travel path within both the upper and lower central bores  135  and  125 , parallel to axis P. Plunger element  118  contains an embedded permanent magnet  126  in probe portion  127 . An aperture  124  is formed ahead of magnet  126  in probe portion  127  to minimize resistance to the flux field emitted by magnet  126 . Probe portion  127  contains a flanged extension  133  at its extreme lower end, adjacent aperture  124 . Flanged extension  133  serves to slide along and to be guided by lower central bore  125  during the linear or axial path movement of probe portion  127  therein. Flanged extension  133  also serves to limit the upward movement of probe portion  127  by catching the transition wall formed between upper central bore  135  and lower central bore  125 . Upper head portion  134  of plunger element  118  contains a lower surface  123  that is oriented towards base support element  120 . Base support element  120  contains a shoulder surface  121  that is oriented in opposition to lower surface  123 . Spring element  111  is mounted between shoulder surface  121  and lower surface  123 . Spring element  111  may be a bellows, a coil spring or any other element that has the desired characteristics. The desired characteristics of spring element  111  are: a predictable spring constant over a long lifetime to allow a predetermined amount of relative movement between magnet  126  and sensor  122  for a known load; and a spring constant that is of sufficient force to restore plunger element  118  to its full height with respect to base support element  120 , when no load is applied. One may also attach the upper head surface  134  to top of cavity  109  through an in-molding process, or otherwise attach the plunger element  118  to the cushion material, and thereby utilize the spring-back characteristics of the cushion material used for seat bun  104  to provide a restoration of plunger element  118  to its no load condition/position. However, by including a spring element  111  to supplement the spring-back characteristics of the seat cushion material, a more robust, reliable and accurate embodiment is attained. This is because current choices of seat cushion materials have inconsistent properties between batches, between manufacturers and over time and therefore may be unreliable to restore the plunger  118  to its proper no-load position. Certainly, if seat materials are developed that have consistent spring-back properties over the expected life of the system and vehicle, then it is possible that the separate spring element  111  could be eliminated form the sensor  108 . 
     Base support element  120  also contains a catch ring  130  formed on its outer periphery. Latch elements  129  extend upwards from substrate  113  through cutouts  220  in flexible circuit layer  112 . Latch elements  129  are arranged to retain catch ring  130  and thereby lock in place base support element  120  and the attached components of weight sensor  108 . 
     In use, plunger element  118  is forced from its no-load or “zero” position along the travel path parallel to axis P, towards sensor  122  when a weight is placed on upper surface  105  of seat bun  104 . 
     Plunger element  118  and also magnet  126 , may move over a predetermined distance “E” that is selected such that the optimum and substantially linear performance of the sensor  122  is achieved over the range of movement between the elements along the defined linear travel path. The predetermined distance E is physically limited in the upper direction by flanged extension  133  catching the transition wall formed between upper central bore  135  and lower central bore  125 ; and in the lower direction by lower surface  123  contacting stop element  131 . It should be noted that the limited predetermined distance E is slightly shorter than the distance “F” which indicates the maximum distance between the magnet  126  and the sensor element  122 . This is purposely designed into the weight sensor  108 , in order to prevent magnet  126  from physically contacting sensor  122  during maximum loading conditions. It is believed that, without his protection, repeated contact could eventually cause breakage of sensor  122 , magnet  126 , flexible circuit layer  112 , and/or plunger element  118 . 
     In  FIG. 2 , sensor  122  is shown mounted on flexible circuit layer  112 , preferably composed of silver or other conductive material traces printed onto or embedded in a sheet of insulated film, such as Polyamide. Flexible circuit layer  112  may be attached to lower surface  103  with a double-sided adhesive sheet or adhesive layer  136 . Substrate  113  may be attached to flexible circuit layer  112  with a double-sided adhesive sheet or adhesive layer  138 . Substrate  113  provides a stable reference position for the sensor  108  and serves to seal the lower surface of the system from the elements, and also protecting the circuit from being cut or punctured by sharp objects that may be present under the seat. Although adhesives are used in this description, is it recognized and anticipated that others may chose to use other mechanisms to attach the elements to the seat cushion and have results similar to ours. 
     In  FIG. 3 , a first array of weight sensors  108 ′ is shown mounted on a rigid substrate  113 ′ for assembly to a seat. Array patterns are selected according to the demands of individual seat or vehicle builders/customers and may take on a variety of specified sensing patterns. 
     In  FIG. 4 , a second array of weight sensors  108 ″ is shown mounted on a rigid substrate  113 ″ prior to assembly in a seat. The flexible circuit layer  112 ″ for the array shown in  FIG. 4  is further detailed in  FIG. 5 . 
     In  FIG. 5 , the flexible circuit layer  112 ″ is detailed in a plan view to show base substrate  202 , power conductor  203 , ground conductor  204 , and sensor output conductor  206 . Base substrate  202  may be a formed from a conventional material such as a polyester or Polyamide laminated to a dielectric layer. The conductors may be formed in a conventional manner, such as by etching or printing of electrically conducting metals such as silver, gold or copper. Alternatively, wires may be embedded in the laminated layer. The conductors are routed to each of the sensor locations, as exemplified at location  208 , and to a tail  207  where they are connected to a power supply and processor in a conventional manner. 
     In  FIG. 6 , a detailed view is provided of sensor location  208 , as shown in  FIG. 5 . In this view, power conductor  203 , ground conductor  204 , and sensor output conductor  206  are shown leading to power pad  213 , ground pad  214  and output pad  216 , respectively. Power terminal  122   p , ground terminal  122   g  and output signal terminal  122   s  extending from the sensor  122  are shown as connected to the respectively corresponding pads  213 ,  214  and  216  in a conventional manner, such as by soldering, ultrasonic bonding or adhesive bonding. Cutout apertures  220  are formed on either side of the sensor  122  in base substrate  202  and are configured to accommodate the latch elements  129  that extend from rigid substrate  113 , as shown in  FIG. 2 . 
     In  FIG. 7 , plot “A” shows the output voltage Vdc characteristics provided by a sensor  122  vs. the size of the air gap between sensor  122  and magnet  126  over the distance F. This portion of the sensor&#39;s output characteristics is selected for monitoring the weight applied to a seat cushion, because it is the closest to a linear output that the Hall-effect sensors produce. Point “C”, at 0.5 inches in this example, shows the maximum separation between magnet  126  and sensor  122  at the preferred no-load condition. Point “D” at 0.125 inches, in this example, shows the minimum separation between magnet  126  and sensor  122  under an assumed fully loaded condition. Plot “B” illustrates the linear approximation that is used by the processor to adjust the actual voltage readings from each sensor  122  at each point along the axial travel path that readings are taken. 
     In  FIG. 8 , the flowchart illustrates the initial power-up process  300  that is used to set the “zero” reference point for the signal readings from each weight sensor, after the weight sensors are installed in a seat cushion and preferably before the seat is installed in a vehicle. This is done with no load present on the seat cushion, in order to calculate/establish the corresponding “zero” reading by each weight sensor  108 . When initial power activates the system after installation of the weight sensor array within the seat and during the seat assembly procedure, the processor  114  detects this as the “first power-up” at step  310 . The processor responsively initiates the data gathering protocol at sub-process  500 , which is described below in association with the more detailed flow chart shown in  FIG. 10 . Sub-process  500  samples and gathers the output reading from each weight sensor  108  in the array. Following the data gathering sub-process  500 , sub-process  600  is used to pre-filter the gathered sensor data, which is described below in association with the more detailed flow chart shown in  FIG. 11 . The pre-filter sub-process  600  serves to provide adjustments to the gathered sensor data, if ambient conditions such as temperature indicate that it is necessary. Following the pre-filter sub-process  600 , the resultant sensor data from each sensor  122  is stored at step  320  as its “zero” point, from which future readings of the respective sensors are compared in order to determine the degree of loading that is being sensed. Following the storage of the sensor zero readings at step  320 , initial power-up process  300  is recorded as being completed at step  330 . 
     In  FIG. 9 , a flow chart shows the categorization process  400  that is used to identify and categorize the occupant/load present on the seat bun  104 . (The following discussion will involve frequent references to other, more detailed flow charts provided in  FIGS. 10 ,  11 ,  12 ,  13  and  14 .) In this example, the system processor  114  initiates the categorization process  400  when the system is turned on, preferably at step  410  by sensing when the ignition or start switch is turned on in the associated vehicle. The first step is to gather data from each weight sensor  108  by sub-process  500 , which is shown in  FIG. 10 . 
     Data gathering sub-process  500  begins with step  510  and proceeds to step  520  where a Read Counter is set to zero and the total sensor value for each of the sensors is set to a zero value. At step  530  all the sensors are read at once and the output signal value for each sensor is added to the total for each sensor. At step  540  the Read Counter is incremented by one count. At step  550 , a determination of whether the Read Counter has been incremented past a predetermined value “N”. In this example, the value of N is 4. This allows the sensors to be read five times so that an average sensor output value can be determined. This procedure of averaging the data over a predetermined number of cycles serves to minimize the effects of electromagnetic or other background interference that may impact the readings from the weight sensors  108 . 
     If the determination at step  550  is that the Read Counter is less than N, steps  530 ,  540  and  550  are repeated until the Read Counter has been incremented to a value greater than N. When step  550  determination is in the affirmative, the data gathering sub-process  500  proceeds to step  560  where the average sensor value is computed for each sensor from the total values produced at step  530 . The average value for each sensor output is stored at step  560  as average sensor data. The data gathering sub-process  500  is completed at  570  and proceeds to the sensor data pre-filtering sub-process  600 , within categorization process  400  (or the initial power-up sub-process  300 , as appropriate). 
     The sensor data pre-filtering sub-process  600  is shown in  FIG. 11  as beginning with step  610 . A temperature reading is taken at step  620  from the ambient temperature sensor  110 . Based on the value of the temperature reading at step  620 , the sensor data pre-filtering sub-process  600  makes adjustments to the sensor data at step  630 . In this example, pre-filtering adjustments are made to the sensor data for each weight sensor according to a predetermination of how such temperature values are known to effect the sensor data. Such pre-filtering adjustments may be necessary when the sensors are heat sensitive and produce varying readings according to variations in temperature; or when the degree of seat compression by predetermined loads is effected by temperature and/or humidity. If humidity is deemed to effect the readings, then a humidity sensor can be installed to provide such data and appropriate pre-filtering will be made to the sensor data at step  640 . Similarly, other ambient conditions that can be sensed and measured as having a predictable effect on the sensor data, can also be pre-filtered in step  640 . When the sensor data pre-filtering sub-process  600  is completed at step  650 , the categorization process  400  proceeds to the “determine if re-zero recalibration is required” sub-process  700  (or to step  320  within the initial power-up process  300 , as appropriate). 
     In  FIG. 12 , the “determine if zero recalibration is required” sub-process  700  is shown as starting at step  710 . This sub-process is repeatedly performed over the life of the system to adjust the base readings from each weight sensor under a no-load condition. This may be necessary in order to accommodate for deteriorating cushion material or for other changes that could otherwise adversely effect the sensor readings. As it is not possible to check for such changes directly, an auxiliary variable, such as operation time of the system or number of ignition cycles of the engine, is used to determine the need for zero recalibration. This variable is examined in step  720 . If there is no need for zero recalibration, the corresponding flag is set to FALSE in step  770 , which completes the sub-process  700 . If condition for zero recalibration requirement is satisfied, the system verifies whether the current control signal indicates an empty seat in step  730 . If the seat is not empty, zero recalibration is explicitly impossible and the flag is set to FALSE in step  770  completing the sub-process  700 . If the seat is empty, the system further verifies whether the zero recalibration is implicitly possible in step  740 . This step is used to determine the difference of the current sensor data and the stored zero calibration values. Ideally the difference should be zero if the seat is empty and there has been no deterioration of the seat or sensor components. In practice this value will be small but nonzero, reflecting the changes in seat bun and/or sensors. However, in some situations, the seat could be occupied by object(s), such us suitcases that are correctly classified as an empty seat due to their weight and pattern characteristics, but cause deflection of the seat bun and produce relatively large nonzero output values of the sensors. For this reason, at step  750  the difference is compared to a predetermined value Y. If the computed difference is less than Y, the corresponding flag is set to TRUE at step  760 . Otherwise, the flag is set to FALSE at step  770  to avoid introduction of bias caused by the presence of light objects during recalibration. The sub-process  700  is completed at step  780  and the processor takes up step  420  in the categorization process  400  shown in  FIG. 9 . 
     Referring to  FIG. 9 , step  420  looks at the rezero required flag and if it is set to True, causes categorization process  400  to proceed to step  430  where the data from the sensors is considered to be at zero values and is so stored. If, on the other hand, the rezero required flag is seen as set to False at step  420 , categorization process  400  is caused to proceed to the compute control signal sub-process  800  as shown in  FIG. 14 . 
     The compute control signal sub-process  800  is the final sub-process in categorization process  400  and provides an output signal that is essential to allowing an associated air bag deployment sub-system to know how the seat load is categorized for an occupant—and therefore, whether or how much to deploy the airbag in the event of a vehicle collision. Since people come in a wide range of shapes and sizes, the processor  114  must be programmed so as to be capable of recognizing weight patterns of various sized human occupants and loads such as infant seats and generalizing them to yield correct output for any occupant or load. A learning system, such as a neural network system, is utilized to provide such functionality in the form of tables that are then referenced by the on-board system processor  114 . The tables of values generated from the neural network in the learning system are referenced by the processor while applying an algorithm that simulates a neural network, and thereby requires less memory and processing power than an actual on-board neural network processor would require. 
     A neural network simulation consists of two basic elements: nodes and connections. Nodes are additive, summing all values from connections entering the node and sending that value to the connections leaving the node. Connections are multiplicative, multiplying a value passing through a connection by the weight associated with it. The signals outputting the node are usually conditioned using a transfer function assisting the neural network in achieving desired nonlinear characteristics. To create the basic architecture for a neural network simulation, nodes and connections are usually arranged into conceptual “layers” of different sizes. The input layer receives the input from the source. Conversely, the output layer creates the output for the user. The size of the input layer and the output layer are determined by the desired amount of inputs and outputs. The hidden layers, so named because they are conceptually hidden from the outside of the network, determine the non-linearity and generalization capabilities of the network. By changing the size of the layers (i.e., the number of nodes), higher resolution and more detail of the pattern may be obtained, thereby allowing a wider variety of patterns/classes to be recognized. At the same time, the size of the layers must be kept as small as possible to keep the minimize the storage and processing requirements of the system and to optimize its generalization capabilities. In practice, the size of the layers are determined experimentally to resolve this trade-off. 
     In  FIG. 13 , a block diagram conceptually represents a neural network  232  . In this example, the architecture of the network  232  contains sixteen individual nodes  234  in the input layer  236 ; M individual nodes  237  in the hidden layer  238 ; and “N” nodes  239  in the output layer  242 , where the value of M is obtained experimentally as described above, and the value of N is given by the desired number of occupant categories (e.g. N=3 for the classification describe in  FIG. 15 ). Each node  234  in the input layer  236  receives at input  244  a preprocessed value from a corresponding weight sensor  108  in the array of such sensors. Each node  234  in the input layer  236  is connected to each individual node  237  in the hidden layer  238  with multiplicative connections  246  each being assigned a weight factor  248 . Every node  237  in the hidden layer  236  is further connected to each node  239  in the output layer  242 . Tables are prepared as a result of the learning process and contain values that respectively correspond to the individual weight sensors and their respective readings. The tables are referenced by sub-processor  114  in order to simulate the network  232  by multiplying, summing, and conditioning the readings according to its algorithm in order to provide an output that is indicative of predetermined classifications and categories of seat occupants, and according to the sensed weight distribution over the weight sensor array. The outputs  254  of the output layer  242  may then be used by the associated airbag deployment system as a control signal as mentioned above. It should be noted that this architecture is not the only available architecture for the simulated network  232 . As seat buns become more complex and as a wider of variety of patterns is to be recognized, the size of the network  232  may grow and change. 
     In  FIG. 14 , the compute control signal sub-process  800  is shown that simulates a neural network protocol and begins at step  810 . At step  815 , the sensor datum is tared for each sensor. Taring is achieved by subtracting the stored zero point from each sensor&#39;s averaged sensor value that is stored in step  560  of  FIG. 10 . The result is a net value that is reflective of a load applied to each weight sensor  108 . At step  820 , the tared sensor data is assigned according to respectively corresponding input nodes. For each hidden node, the tared sensor data value at each assigned input node is respectively multiplied by a factor from a table designated WI to derive corresponding input products, at step  825 . In simulating a neural network, each input value is multiplied by a distinctly addressed factor read from table WI at step  825 . The WI factors are addressed according to the unique identity of each hidden node. Following the multiplication at step  825 , all corresponding input products are summed for each hidden node in step  830 . For each hidden node, a value is added to the sum that is derived from a table designated WBI at step  835 . The WBI values are addressed according to the unique identity of each hidden node. A sigmoid function is then taken at step  840  of the value derived at step  835  for each hidden node, in order to derive a corresponding hidden node value. For each output node, the hidden node value at each hidden node is respectively multiplied by a factor from a table designated WII to derive corresponding hidden products, at step  845 . In continuing to simulate a neural network, each hidden node value is multiplied by a distinctly addressed factor read from table WII at step  845 . The WII factors are addressed according to the unique identity of each output node. Following the multiplication at step  845 , all corresponding hidden products are summed for each output node in step  850 . For each output node, a value is added to the sum that is derived from a table designated WBII at step  855 . The WBII values are addressed according to the unique identity of each output node. A sigmoid function is then taken at step  860  of the value derived at step  855  for each output node, in order to derive a corresponding output node value. At this stage of sub-process  800 , a comparison step  865  is performed in order to determine which output node has the greatest output node value. The control signal is than set to correspond to the output node with greatest value at step  870 . The sub-process  800  is completed at step  875 , thereby returning to step  440  in categorization process  400  of  FIG. 9 . 
     Again referencing  FIG. 9 , the categorization process  400  shows that in step  440  the current control signal derived by sub-process  800  is stored for access by an associated deployment control system, or by any other system that requires to know the categorization of an occupant or load present above the sensor array. 
     In  FIG. 9 , it can be seen that the categorization process  400  is repeated at predetermined intervals following step  440  during the time the vehicle is in operation. Categorization determinations are made with each cycle of the process in order to provide a control signal to the associated deployment control system. The associated deployment control system then uses the control signal stored at step  440  to deploy or not deploy the airbag according to the classification as mandated and shown in  FIG. 15 . 
     Although  FIG. 15  shows the current mandate for classification into three classes based upon sensed occupant characteristics, it is anticipated that in the future, as airbag deployment force, size and speed is more precisely controlled, there may be several more classes for degraded deployment based upon the sensed occupant characteristics described in  FIG. 15 . 
     It should be understood that the foregoing description of the embodiments is merely illustrative of many possible implementations of the present invention and is not intended to be exhaustive.

Technology Category: 7