Abstract:
A multiple sensor, vehicle seat occupant status determination system uses dynamic variations in occupant responsive sensor output due to accelerations of the vehicle seat to discriminate via a statistical method between sensors that are failed and those that are operating correctly and eliminates the contribution of signals detected as not valid from the status determination. If and only if the maximum and minimum sampled signal values of at least one of the received signals span upper and lower control reference values derived from the sampled signal values, a variance value for each of the signals is derived; and one or both of the greatest two or the smallest two variance values are checked against a relative size factor to detect an outlier signal value. If an outlier signal value is detected more than a predetermined number of consecutive times, the status classification is performed without signal values from the outlier sensor.

Description:
RELATED APPLICATIONS 
     This application is related to U.S. Provisional Application No. 60/408,039, filed Sep. 4, 2002, and to U.S. patent application Ser. No. 10/653,463, entitled Seat Belt Tension Determination Using Multiple Seat Belt Tension Sensors, filed on the same date as this application. 
    
    
     TECHNICAL FIELD 
     The technical field of this invention is vehicle restraint systems. 
     BACKGROUND OF THE INVENTION 
     A vehicle seat occupant status classification system uses sensors in or associated with the seat to detect physical variables indicative of the presence, position, weight, capacitance or other physical characteristics of a seat occupant, in order to help decide whether or not to deploy a passive restraint such as an airbag during a crash event. These sensors are subject to failures during the useful life of the vehicle, the number of such failures being limited, but impossible to totally eliminate, in spite of the best known quality control. Some such failures may of the “stuck” sensor type, wherein the sensor itself is operable but cannot read the true value of the physical characteristic due, for example, to an object wedged under the seat. Others may be due to an electrically or mechanically failed sensor producing a floating signal having no relationship to the physical characteristic. The use of multiple, and to some extent redundant, sensors can provide the system with a reasonable level of operability even with one or more sensors failed or otherwise not providing an accurate output signal; but system performance may be somewhat degraded as a result. 
     SUMMARY OF THE INVENTION 
     This invention provides detection of individual failed sensors in a multiple sensor, vehicle seat occupant status classification system. The invention makes use of dynamic variations in sensor output due to accelerations of the vehicle seat to discriminate via a statistical process control method between sensors that are failed and those that are operating correctly; and further eliminates the contribution of signals detected as failed from the final status classification. 
     The method and apparatus of this invention determines a classification of a vehicle seat occupant by receiving signals for a predetermined time from a plurality of sensors responsive to a physical characteristic of an occupant at diverse locations on a vehicle seat apparatus, determining a vehicle seat occupant characterization from the received signals in a predetermined classification process, and deriving maximum and minimum sampled values of each of the received signals during the predetermined time and comparing the derived maximum and minimum sampled values for each of the received signals to defined upper and lower control reference values. 
     If and only if the maximum and minimum sampled values of at least one of the received signals spans the upper and lower control limits for that received signal, (a) a variance value is derived for each of the received signals for the predetermined time, (b) two of the received signals having derived variance values that are a selected one of larger and smaller than the variance values of the remaining received signals are identified, (c) a ratio of the derived variance values of the two of the identified signals is compared with a first relative size factor, and (d) if the ratio exceeds the relative size factor, one of the two identified received signal having a variance value that is furthest in magnitude from the remainder of the variance values is marked as an outlier. 
     Finally, the status classification is determined from the received signals in a predetermined classification process, but without the use of any received signal marked as an outlier more than a predetermined number of consecutive times. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention will now be described, by way of example, with reference to the accompanying drawings, in which: 
         FIG. 1  shows a side view of a vehicle seat equipped with a plurality of sensors responsive to a physical characteristic of an occupant. 
         FIG. 2  shows a downward view along lines  2 — 2  of  FIG. 1 . 
         FIG. 3  shows a block diagram of a vehicle restraint deployment apparatus for use with the seat of  FIGS. 1 and 2 . 
         FIG. 4  shows a flow chart of a high level description of the operation of the embodiment shown in  FIG. 3 . 
         5 A– 5 B and  6  show flow charts describing in more detail the operation of certain subroutines in the process of  FIG. 4 . 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring to  FIGS. 1 and 2 , a vehicle seat apparatus  10  comprises a seat cushion  12  and seat back  13  supported by a seat frame  14  comprising, for example, seat rails  15  and  16 . A plurality of occupant characteristic sensors  20 ,  22 ,  24 ,  26  are provided as part of vehicle seat apparatus  10  to sense a physical characteristic, such as weight, of a mass  18  such as an occupant or an object on seat cushion  14 . In the described embodiment, sensors  20 ,  22 ,  24  and  26  comprise strain gauges or similar sensors measuring the strain at four corners of the seat frame, with sensors  20  and  22  fore and aft, respectively, on left seat rail  15  and sensors  24  and  26  fore and aft, respectively, on right seat rail  16 . 
       FIG. 3  shows a passive restraint deployment control system for seat  10  comprising an occupant detection module  40 , a restraint (in this embodiment, airbag) control module  42  and a restraint  44 . Modules  40  and  42  are electronic controls, preferably algorithms embodied in programs on one or more digital computers. Module  40  receives signals from occupant sensors including sensors  20 ,  22 ,  24  and  26  and determines an occupant status for seat  10 . A signal based on the determined occupant status is provided by module  40  to module  42 , which also receives crash signals from crash sensors, not shown, and determines if and when to deploy restraint  44  to protect the occupant of seat  10 . 
     A preferred embodiment of the computer routine of occupant detection module  40  is shown in  FIG. 4  and entitled STATUS CLASSIFICATION. The loop shown is run repeatedly at, for example, every 100 milliseconds. Beginning at step  70 , the routine reads and stores A/D converted input values from the sensors, including sensors  20 – 26 . At step  72  the routine determines if it is time to determine occupant status, which occurs, for example, once every six seconds (once every 60 loops). If not, the routine returns for the next loop. But if it is, a subroutine DETERMINE OCCUPANT STATUS is called at step  74  to perform an occupant status classification according to whatever criteria have been established. In the simplest case, only two status classifications might be established: for example (1) adult occupant and (2) child or no occupant. One known type of system derives this status classification from a sensed occupant weight; and the flow chart is written for this simple case as an illustration. But variations will occur to those skilled in the art: for example, more than two status classifications (e.g. three: adult, child or none) or classifications based on physical characteristics other than weight, such as capacitance. The subroutine will be described at a later point with reference to  FIG. 6 . 
     After determining occupant status, the routine determines at step  76  if it is time to perform diagnostics. This occurs at a lower frequency, since it involves statistical calculations requiring a significant number of input values from each sensor to detect an invalid sensor that should be ignored. In this embodiment, this will occur every 60 seconds (once every 600 loops). This function is performed at step  78  with the calling of subroutine PERFORM DIAGNOSTICS, which will be described in connection with  FIGS. 5A–5B . Upon a return from this subroutine, routine STATUS CLASSIFICATION is also finished and returns for the next loop. 
     Subroutine DETERMINE OCCUPANT STATUS is shown in flow chart form in  FIG. 6  for the specific embodiment of a weight based status classification, so as to fit with the example of  FIGS. 2–3  using seat frame based strain sensors as occupant weight responsive sensors. Various algorithms for such weight based determination are known in the art; and the range of sensors used, in addition to the strain sensors shown herein, pressure sensor(s) in a fluid filled bladder and resistive or capacitive force responsive sensors integrated with the seat cushion or cover, among others. Beginning at step  90 , an occupant weight is calculated, for example based on a computed average or other weighted sum of all valid sensor readings from sensors  20 – 26 . Each sensor is assigned a validity flag in software that begins set valid but may be cleared to an invalid status in the PERFORM DIAGNOSTICS subroutine as described hereinafter. At step  90  only those sensors marked valid by their validity flags are used in the weight determination. At step  92 , the calculated weight is adjusted for any environmental variables that might affect the sensor readings relative to their factory calibrations: such as, for example, temperature, humidity, etc. 
     At step  94 , the adjusted calculated weight is compared with one or more stored reference values, which may also be adjusted in response to environmental variables. Depending on the result of step  94 , the occupant is assigned, in a simple case, a first status at step  96  or a second status at step  98 . In this embodiment, the first status would correspond to an adult occupant and would ordinarily result in allowance of restraint deployment in a crash; and the second status would correspond to a child or no seat occupant and would ordinarily result in no restraint deployment in a crash. Of course, if there are three or more occupant status possibilities defined (as described, for example, above with reference to step  74 ), the flow chart would be expanded to provide multiple comparisons using ranges according to the predetermined criteria. Finally, at step  99 , a status message is prepared for communication to airbag control module  42 . 
     The flow chart and process described thereby are not limited to weight based sensors. For example, some capacitive sensors are responsive to the physical capacitance of a seat occupant (rather than being weight responsive capacitive transducers) and can be used to determine an occupant&#39;s position with respect to the seat. In such a case, the subroutine would use known methods to determine the occupant&#39;s position from multiple capacitive sensors and determine whether or not the occupant is in a position appropriate for restraint deployment or one that is not. 
     The function of subroutine PERFORM DIAGNOSTICS, introduced at step  74  above and described in detail with reference to  FIGS. 5A–5B , is to determine, using certain statistical control processes which, if any, of a plurality of the sensor signals  20 – 26  should be at least temporarily ignored because its dynamic performance over time is not within a predetermined range of agreement with the other sensors. Beginning at step  100 ,  FIG. 5A , the routine determines if the vehicle is moving. If it is not yet moving, there will be no dynamic variation that is meaningful to the algorithm (and also no need for determination); so the subroutine exits. 
     If the vehicle is moving, the subroutine continues to step  104  and derives upper and lower control references for each sensor. These are derived from the accumulated signal values from each sensor, as one would derive process control limits to distinguish normal process variations from the larger process variations due to specific causes, as in the Six Sigma (R) or similar methods. The purpose of these upper and lower control references is to help determine if there is sufficient dynamic variation due to external forces to continue with the algorithm. Thus, at step  106 , the maximum and minimum ones of the accumulated values for each sensor are compared with the upper and lower control reference values, respectively, for the corresponding sensor. If, for at least one of the sensors, the maximum and minimum values span the upper and lower control references—that is, the maximum sensor value during the period exceeds the upper control limit and the minimum sensor value during the period is exceeded by the lower control limit—then there is sufficient dynamic variation from an external source; and the subroutine continues to step  108 . But if this is not true for at least one of the sensors, there is insufficient dynamic variation to apply the method of this invention; and the subroutine is thus exited. This could occur if the vehicle is being driven conservatively on a very smooth road, so that little external force variation is provided to seat  10  and thus affecting the occupant responsive sensor signals. 
     If at least one of sensors  20 – 26  meets the requirements of step  106 , the subroutine proceeds to step  108  and calculates an estimate of a variance value (such as the variance, the standard deviation, or either divided by a nominal or average value) for each of the sensors. The subroutine then determines at step  110  if the minimum variance value of the sensors is a factor X less than the next to minimum variance value. If not, then no minimum outlier is identified; and the subroutine at step  112  performs a mirror image of this test for the maximum variance value. If the maximum variance value of the sensors is not a factor X greater than the next to maximum variance value, subroutine clears a number COUNT at step  113  and returns, since no outlier has been found. 
     From either of steps  110  or  112 , if the answer is yes, the subroutine continues via tab A to step  114 , wherein it increments the value of COUNT. The number COUNT represents the total number of consecutive loops of the subroutine resulting in an outlier being found. Before a sensor is declared not valid, it must be found to be an outlier a predetermined number of consecutive times. The value of COUNT is next tested at step  116  against a predetermined reference REFCNT, which may be, for example, 3. If the value of COUNT does not exceed REFCNT at step  116 , the subroutine returns; but if it does exceed REFCNT, the outlier sensor is marked as not valid at step  118 ; and a fault is registered at step  120 . Finally, the value of COUNT is cleared to zero before the subroutine returns to enable tests for another sensor that is not valid. 
     In this manner, occupant responsive sensors having certain faults can be identified and their outputs ignored in vehicle seat occupant classification based on an occupant physical characteristic. For the frame based sensors shown in this embodiment, the types of faults detected include stuck sensors, a connection failure between the sensor and the seat frame and limits on sensor output due to items stuck under the seat system. The method can also be applied to capacitive sensing systems using sensors responsive to electric field changes induced by the proximity of an occupant. The types of failures detected include fluid spills over the system, mechanically locked plate position and loss of electrical conductivity. Pattern recognition systems having multiple sensors distributed over the surface of a seat can be improved by this invention, for several sensor technologies (capacitive, resistive, etc.). Faults detected are physically inverted or twisted sensors, mechanical or electrical interference to a sensor or group of sensors, thermal or mechanical set of a sensor and sensor lock into a foam void.