Patent Publication Number: US-2010121536-A1

Title: Performance-based classification method and algorithm for passengers

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of the filing date of U.S. Provisional Application Ser. No. 61/113,915, titled Performance-Based Classification Method and Algorithm for Passengers, filed Nov. 12, 2008. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates generally to a system and method for classifying passengers by relating passenger body size and vehicle setting information to the best possible crash safety performance that could be provided by a select set of occupant protection designs, and more particularly, to a system and method for classifying passengers by relating body mass index and seating position information to the best possible crash safety performance. A control algorithm is also proposed using the method to enable a vehicle to automatically select the best occupant protecting design for individual passengers. 
     2. Discussion of the Related Art 
     Modern vehicles often include systems for automatically setting various components and features in the vehicle for a particular vehicle driver and/or passenger, many of which are based on the size of the driver and the personal preferences of the driver. Particularly, modern vehicles are generally designed to allow persons of varying sizes and preferences to adjust features of vehicle systems for each person&#39;s comfort, convenience and operation needs. These vehicle features can include vehicle seats, foot pedals, rear-view mirrors, steering columns, etc. To reduce the burden of readjusting the selected features of a vehicle, some vehicles employ a memory system that stores the preferred settings for one or more users that is configured to automatically adjust the vehicle systems to the preferred settings upon request. 
     Modern vehicles also include a number of safety devices that protect the vehicle occupants during a crash event, such as airbag systems and seatbelt systems. Vehicle airbag systems are complex systems that are designed to protect the vehicle occupants. For example, airbag systems need to be designed so that they are not activated unless the crash event is significant enough, they are not activated unless the crash event is from the proper direction, the airbag is deployed fast enough during the crash event, the airbag is filled with enough gas to protect the vehicle occupant during the crash event and the airbag is properly vented so that the gas can escape from the airbag with the proper flow rate when the vehicle occupant is forced against the airbag so as dissipate the kinetic energy of the occupant without causing high rebound speed. 
     Vehicle seatbelt systems may be also equipped with a load-limiter that limits the load on the seatbelt so that it provides proper restraint forces to protect the belted occupant in a crash event. Particularly, during a crash event where the seatbelt wearer may be forced into the seatbelt with high inertia force, the load-limiter allows the seatbelt to extend or give a certain amount so that the seatbelt force during the event is high enough to provide the needed restraint, but not to cause injury to the wearer. 
     Typically, the passenger airbag filling and venting rate, the seatbelt load-limiter tension and other safety features in the vehicle are set for an “average” person sitting at a “mid” position and may not be optimized for persons of lower weights and sizes and persons of higher weights and sizes and/or for persons at a non-“mid” seating position. Therefore, it would be ideal to provide a system and method that personalizes the passenger safety features on a vehicle for every different combination of individuals and seating positions that can be set and stored much in the same way as other vehicle features. 
     Practically, it may be desirable to provide a classification system and method that personalizes the passenger safety features on a vehicle to only a finite set of classes for different clusters of combinations of individuals and seating positions that can be set and stored much in the same way as the other vehicle features referred to above. 
     SUMMARY OF THE INVENTION 
     In accordance with the teachings of the present invention, a system and method are disclosed for classifying passengers of a vehicle based on the passenger seat position and passenger body mass index. The method includes determining a number of basic passenger sizes based on the passenger height and mass and determining a number of passenger seat positions. The method further includes identifying a set of tunable design variables that are used to adjust the vehicle safety features, and performing design optimization analysis for identifying optimal designs for the vehicle safety features for each of the basic passenger sizes and the predetermined seat positions (called “basic optimal designs” hereon). The method also produces a predetermined number of randomly selected reference passengers in randomly selected seating positions, and performs design analysis for identifying the best design out of the basic optimal designs for the randomly selected reference passengers. The method identifies the design from the basic optimal designs that provides the best performance for each of the randomly selected reference passengers, and classifies all passengers into one of the predetermined number of classifications where each classification represents a particular basic optimal design. A control algorithm then sets the vehicle safety features for a particular passenger based on a passenger seat position and the passenger&#39;s body mass index using the classification and basic optimal designs. 
     Additional features of the present invention will become apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a side plan view of a vehicle driver in a driver seat of a vehicle; 
         FIG. 2  is a graph with mass on the horizontal axis and height on the vertical axis showing a classification process for different size individuals; 
         FIG. 3  is a graph with time on the horizontal axis and airbag pressure on the vertical axis showing graph lines for different vent sizes and time delay durations of a vehicle airbag; 
         FIG. 4  is a graph with belt elongation on the horizontal axis and belt load on the vertical axis showing a response for a seatbelt load-limiter; 
         FIG. 5  is a graph with occupant mass on the horizontal axis and occupant height on the vertical axis showing the location for optimal design classifications for a 5 th  percentile female, a 50 th  percentile female, a 50 th  percentile male and a 95 th  percentile male; 
         FIG. 6  is a graph with occupant mass on the horizontal axis and occupant height on the vertical axis showing fifty randomly selected individuals; 
         FIG. 7  is a graph with occupant mass on the horizontal axis and occupant height on the vertical axis showing the fifty randomly selected individuals in the graph of  FIG. 6  as classified by the classifications shown in  FIG. 5 ; 
         FIG. 8  is a graph with occupant mass on the horizontal axis and occupant height on the vertical axis showing threshold lines for classifying the data points of the individuals into the four classes; 
         FIG. 9  is a flow chart diagram showing a process for selecting the proper safety feature design for a particular driver of a vehicle; 
         FIG. 10  is a graph with body mass on the horizontal axis and standing height on the vertical axis showing a number of data points for different individuals and a design group that they would fall into relative to a classification for a 5 th  percentile female, a 50 th  percentile female, a 50 th  percentile male and a 95 th  percentile male; 
         FIG. 11  is a graph with mass on the horizontal axis and height on the vertical axis showing data points of individuals falling within different design classifications for a particular seating position of a passenger seat of the vehicle; 
         FIG. 12  is a graph with mass on the horizontal axis and height on the vertical axis showing the classification for the different individuals for another seating position of the passenger seat of the vehicle; 
         FIG. 13  is a graph with body mass index on the horizontal axis and seat position on the vertical axis showing seven design classifications relative to threshold lines for different individuals based on their body mass index and seat position; and 
         FIG. 14  is a flow chart diagram showing a process for selecting the design classification for a particular passenger. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     The following discussion of the embodiments of the invention directed to a system and method for classifying and optimizing safety features of a vehicle based on a passenger seat position and the passenger body mass index is merely exemplary in nature, and is in no way intended to limit the invention or its applications or uses. 
       FIG. 1  is a side plan view of the driver seat area  10  of a vehicle showing a driver  12  sitting in a driver&#39;s seat  14 . The vehicle includes a driver airbag system  16  typically mounted within a steering wheel  18  of the vehicle. The driver&#39;s seat  14  includes a seatbelt  20  having a load-limiter  22  of the type discussed above. The vehicle seat  14  also includes a seat positioner  24  that positions the seat  14  forward and backward in the seat area  10 . 
     The present invention proposes a process for classifying vehicle drivers and/or passengers so that vehicle safety systems, such as airbag deployment sensing time delay and vent size and seatbelt load-limiter force level, are optimized for a particular individual. In one embodiment, the process first identifies body measures of a vehicle occupant, the driver in this case, that are crucial to an outcome of a crash event. In the discussion below, these body measures are occupant height and mass, which can be obtained in any suitable manner. Next the process determines the number of basic occupant sizes n from a distribution of population sizes using the body measures. The driver population distribution of each gender can be provided by statistics data collected by the National Health and Nutrition Examination Survey (NHANES). In one non-limiting embodiment, the method chooses four basic occupant sizes n based on body height and mass, particularly a 5 th  percentile female (F5), a 50 th  percentile female (F50), a 50 th  percentile male (M50) and a 95 th  percentile male (M95).  FIG. 2  is a graph with mass on the horizontal axis and height on the vertical axis showing the distribution of individuals for these basic sizes based on height and mass. 
     The process then creates occupant crash models for each selected basic occupant size n. 
     The process then determines the seating position for each basic occupant size n based on his or her standing height and vehicle design data by assuming a drivers seating position is approximately proportional to his/her height. 
     The process then chooses a set of dynamical tunable design variables for each particular occupant protection system, such as airbag vent size, the time delay duration between the first and second stages of the driver side airbag and seatbelt load-limiter force level.  FIG. 3  is a graph with time on the horizontal axis and airbag pressure on the vertical axis showing the deployment of the airbag system  16  for different time delays.  FIG. 4  is a graph with length on the horizontal axis and seatbelt load on the vertical axis showing seatbelt elongation for different seatbelt loads as provided by the load-limiter  22 . 
     The process then performs design optimization analysis and identifies the basic optimal design for each basic occupant size n. Table I below shows resultant data for basic optimal designs 1-4 representing classification F5, F50, M50 and M95, respectively, and  FIG. 5  is a graph with occupant mass on the horizontal axis and occupant height on the vertical axis showing the relative location for each design classification F5, F50, M50 and M95. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE I 
               
               
                   
               
               
                   
                   
                 2 nd   
                   
                   
               
               
                   
                   
                 Stage 
                 Seat Belt 
               
               
                 Optimal 
                 AirbagVent 
                 Delay 
                 Load Limiter 
                 Occupant 
               
               
                 Design 
                 (multiplier) 
                 (msec) 
                 (kN) 
                 Size 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 1 
                 7.2 
                 5 
                 2300 
                 F5 
               
               
                 2 
                 7.1 
                 10 
                 3500 
                 F50 
               
               
                 3 
                 6.9 
                 10 
                 4400 
                 M50 
               
               
                 4 
                 5.0 
                 25 
                 6000 
                 M95 
               
               
                   
               
            
           
         
       
     
     The algorithm then selects M random reference occupants that represent the occupant population. In one non-limiting embodiment, the number of reference occupants selected is fifty. Crash models are created for each reference occupant size and performance analysis is conducted using the noptimal designs.  FIG. 6  is a graph with occupant mass on the horizontal axis and occupant height on the vertical axis showing the fifty random occupant sizes relative to the design classifications F5, F50, M50 and M95. 
     The process then identifies which design out of the four optimal designs best fits each of the M reference occupant sizes.  FIG. 7  is a graph with occupant mass on the horizontal axis and occupant height on the vertical axis showing how the different reference occupant sizes are categorized into the particular optimal design. 
     The process then classifies the reference occupant sizes into the n body classes.  FIG. 8  is a graph with occupant mass on the horizontal axis and occupant height on the vertical axis showing the classification of the reference occupants shown in  FIG. 6 . In this classification, class 1 is for basic optimal design 1, class 2 is for basic optimal design 2, class 3 is for basic optimal design 3 and class 4 is for design 4. 
     In  FIG. 8 , a threshold line  34  separates class 1 from class 2, a threshold line  36  separates class 2 from class 3 and a threshold line  38  separates class 3 from class 4. In order to determine which classification a new driver fits into, the threshold lines  34 ,  36  and  38  can be defined by the following equations. 
         b   1   =−m   i   x+y    (1) 
         b   2   =−m   2   x+y    (2) 
         b   3   =−m   a   x+y    (3) 
     Where x and y are the driver&#39;s body mass and height, respectively, and m 1 , m 2  and m 3  are the slope of the threshold lines  34 ,  36  and  38 , respectively. For this non-limiting example, b 1 =211, b 2 =226, b 3 =257 and m 1 =m 2 =m 3 =−1. 
       FIG. 9  is a flow chart diagram  40  showing a performance-based driver classification algorithm for a vehicle with individual safety systems, using the classification discussed above. The algorithm first determines whether a driver has entered the vehicle at box  42  by any suitable technique. When the driver enters the vehicle, the algorithm obtains the driver&#39;s height and body mass information at box  44  by any suitable technique, such as having the vehicle driver specifically input the information. 
     The algorithm then calculates a classification quantity C1 for class 1 using equation (1) at box  46 , where C1=−m 1 x+y. The algorithm then determines whether the classification quantity C1 is less than the threshold value b 1  at decision diamond  48 , and if it is, meaning that the classification quantity C1 is less than or equal to the value b 1 , the algorithm determines that the driver is a class 1 driver at box  50 . The algorithm then reconfigures the vehicle safety systems using basic optimal design 1 at box  52 . 
     If the classification quantity C1 is not less than the threshold value b 1  at the decision diamond  48 , the algorithm calculates a classification quantity C2 using equation (2) at box  54 , where C2=−m 2 x+y. The algorithm then determines whether the classification quantity C2 is less than the threshold value b 2  at decision diamond  56 , and if it is, meaning that the classification quantity C2 is between the values b 1  and b 2 , the algorithm determines that the driver is a class 2 driver at box  58 . The algorithm then reconfigures the vehicle safety systems using basic optimal design 2 at box  60 . 
     If the algorithm determines that the classification quantity C2 is not less than the threshold value b 2  at the decision diamond  56 , then the algorithm calculates a classification quantity C3 using equation (3) at box  52 , where C3=−m a x+y. The algorithm then determines whether the classification quantity C3 is less than the threshold value b 3  at decision diamond  64 , and if it is, meaning that the classification quantity C3 is between the values b 2  and b 3 , the algorithm determines that the driver is a class 3 driver at box  66 . The algorithm then reconfigures the vehicle safety systems using basic optimal design 3 at box  68 . 
     If the algorithm determines that the classification quantity C3 is not less than the threshold value b 3  at the decision diamond  54 , the algorithm determines that the driver is a class 4 driver at box  70  and sets the vehicle safety systems using basic optimal design 4 at box  72 . 
     The technique discussed above for determining safety system settings for the vehicle driver assumes that the driver will set the position of the seat  14  based on his/her height, and thus the classification designs for the safety systems will be set accordingly. For a vehicle occupant in the passenger seat of the vehicle, the passenger seat may not be set according to the passenger&#39;s height for various reasons, such as a tall person sitting in the back seat behind them. Therefore, determining the optimal safety feature settings for a vehicle occupant in the passenger seat requires a different analysis to that of the driver discussed above. In one embodiment, the size of the passenger is determined by the position of the seat and the body mass index (BMI) of the passenger, which is body mass divided by body height squared. The process for determining the classifications for the safety feature settings, and then determining which class the passenger falls under is as follows. 
     The process first identifies the desired body measures of a passenger, which are body height and body mass. The process then chooses the total number of basic occupant sizes n, which is the same as for the driver discussed above, with consideration of the distribution of population sizes using the body measures. The process then determines the number of selected seat positions L, such as three, forward, mid and rearward. 
     The process then creates occupant crash models for each basic occupant size n at each selected seat position L. In one non-limiting embodiment, twelve designs are provided based on four basic occupant sizes n and the three seat positions L. The twelve designs include a forward seat position for a 5 th  percentile female (F5 forward), a mid-seat position for a 5 th  percentile female (F5 mid), a rearward seat position for a 5 th  percentile female (F5 rearward), a forward seat position for a 50 th  percentile female (F50 forward), a mid-seat position for a 50 th  percentile female (F50 mid), a rearward seat position for a 50 th  percentile female (F50 rearward), a forward seat position for a 50 th  percentile male (M50 forward), a mid-seat position for a 50 th  percentile male (M50 mid), a rearward seat position for a 50 th  percentile male (M50 rearward), a forward seat position for a 95 th  percentile male (M95 forward), a mid-seat position for a 95 th  percentile male (M95 mid) and a rearward position for a 95 th  percentile male (M95 rearward). 
     The process then performs design optimization analysis and identifies the optimal design for each basic occupant size n at each seat position L, called basic optimal designs hereon. The process chooses a set of dynamical design variables of the occupant protection system, such as airbag vent size and the time delay between the first and second stages of the passenger&#39;s side airbag, and seatbelt load-limiter force level. Table II below shows one set of results of the optimization analysis for the twelve optimal designs for airbag vent position, 2 nd  stage airbag delay and seatbelt load-limiter force level. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE II 
               
               
                   
               
               
                   
                   
                 Inflator 2 nd   
                 Seat belt 
                 Occupant Size 
               
               
                 Optimal 
                 Vent 
                 Stage Delay 
                 limiter 
                 &amp; Seating 
               
               
                 Design 
                 (multiplier) 
                 (msec) 
                 (kN) 
                 Position 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 1 
                 2.62 
                 10 
                 2780 
                 F5 forward 
               
               
                 2 
                 0 
                 Infinite 
                 2300 
                 F5 mid 
               
               
                 3 
                 7.2 
                 Infinite 
                 2300 
                 F5 rearward 
               
               
                 4 
                 1.92 
                 20 
                 3690 
                 F50 forward 
               
               
                 5 
                 1.5 
                 10 
                 4010 
                 F50 mid 
               
               
                 6 
                 1.21 
                 10 
                 4420 
                 F50 rearward 
               
               
                 7 
                 2.04 
                 30 
                 2310 
                 M50 forward 
               
               
                 8 
                 2.85 
                 30 
                 4880 
                 M50 mid 
               
               
                 9 
                 2.62 
                 30 
                 5180 
                 M50 rearward 
               
               
                 10 
                 2.27 
                 25 
                 5810 
                 M95 forward 
               
               
                 11 
                 2.17 
                 25 
                 5950 
                 M95 mid 
               
               
                 12 
                 1.59 
                  5 
                 5980 
                 M95 rearward 
               
               
                   
               
            
           
         
       
     
     The process then looks at the basic optimal designs and their crash performance results to consolidate or reduce the number of basic optimal designs to a smaller set, if possible. Table III shows that the twelve designs can be readily reduced to seven basic optimal designs, namely designs 4-6, 8-10, and 12. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE III 
               
               
                   
               
               
                   
                   
                 Inflator 2 nd   
                   
                 Occupant Size 
               
               
                 Optimal 
                 Vent 
                 Stage Delay 
                 Seat Belt 
                 &amp; Seating 
               
               
                 Design 
                 (multiplier) 
                 (msec) 
                 limiter (kN) 
                 Position 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 4 
                 1.92 
                 20 
                 3690 
                 F50 forward 
               
               
                 5 
                 1.5 
                 10 
                 4010 
                 F50 mid 
               
               
                 6 
                 1.21 
                 10 
                 4420 
                 F50 rearward 
               
               
                 8 
                 2.85 
                 30 
                 4880 
                 M50 mid 
               
               
                 9 
                 2.62 
                 30 
                 5180 
                 M50 rearward 
               
               
                 10 
                 2.27 
                 25 
                 5810 
                 M95 forward 
               
               
                 12 
                 1.59 
                 5 
                 5980 
                 M95 rearward 
               
               
                   
               
            
           
         
       
     
     The process then determines a desired number of reference occupant sizes M and randomly selects the reference occupants as a reasonable distribution based on the real-world population. In one non-limiting embodiment, the number of reference occupants selected is sixty-five. The process randomly distributes the seating position of each reference occupant.  FIG. 10  is a graph with body mass on the horizontal axis and standing height on the vertical axis showing distributions for the randomly selected occupants for the seven designs and four occupant sizes F5, F50, M50 and M95. Crash models are created for each reference occupant at a particular seating position and performance analysis is conducted using the basic optimal designs. 
     The process then identifies the design that yields the best performance out of the seven basic optimal designs for each reference occupant at the chosen seating position.  FIGS. 11 and 12  are graphs with body mass on the horizontal axis and height on the vertical axis showing occupant clustering for the seven basic optimal designs for a seating zone  1  and a seating zone  2 , respectively. Seating zone  1  is the seating zone before the mid-range of the entire seating position and seating zone  2  is the seating zone after the mid-range of the entire seating position. 
     The process then clusters the reference occupants at different seating positions with the same best optimal design.  FIG. 13  is a graph with body mass index on the horizontal axis and seat position on the vertical axis showing the clustering of the reference occupants and the four occupant sizes for the seven basic optimal designs. This graph is used to provide classification C1, C2, C3, C4, C5, C6 and C7 that will set the optimal safety feature positions for the passenger. As above, a threshold line  80  separates class C1 from class C2, a threshold line 82 separates class C2 from class C3, a threshold line  84  separates class C3 from class C4, a threshold line  86  separates class C4 from class C5, a threshold line  88  separates class C5 from class C6 and a threshold line  90  separates class C6 from class C7. Threshold equations are determined for each class C 1 -C 7  as: 
         b   1   =−m   1   x+y    (4) 
         b   2   =−m   2   x+y    ( 5 ) 
         b   3   =−m   a   x+y    (6) 
         b   4   =−m   4   x+y    (7) 
         b   5   =−m   5   x+y    (8) 
         b   6   =−m   6   x+y    (9) 
     Where x and y are the passenger&#39;s body mass index and the seating position, respectively, and m i  is the slope of the threshold lines  80 - 90 . In this embodiment, b 1 =1.833, b 2 =2.067, b 3 =2.347, b 4 =2.427, b 5 =2.713, b 6 =2.833 and m 1 =m 2 =m 3 =m 4 =m 5 =m 6 =−0.067. 
     Once the classifications C1-C7 are defined, an algorithm can be provided that sets the safety features for the passenger in the same manner as discussed above for the driver.  FIG. 14  is a flow chart diagram  100  showing such an algorithm. At box  102 , the algorithm determines whether a passenger has entered the vehicle. If a passenger has entered the vehicle at the box  102 , the algorithm obtains the passengers height and body mass and determines the passenger seat position at box  104 . 
     The algorithm then calculates the passenger&#39;s body mass index and classification quantity C1 using equation (4) at box  106 , where C1=−m 1 x+y, and determines whether the classification quantity C1 is less than the threshold value b 1  at decision diamond  108 . If the classification quantity C1 is less than the threshold value b 1  at the decision diamond  108 , then the algorithm determines that the passenger is a class 1 passenger at box  110  and sets the vehicle safety systems for basic optimal design 1 at box  112 . 
     If the classification quantity C1 is not less than the threshold value b 1  at the decision diamond  108 , then the algorithm calculates the classification quantity C2 using equation (5) at box  114 , where C2 32  −m 2 x+y, and determines whether the classification quantity C2 is less than the threshold value b 2  at decision diamond  116 . If the classification quantity C2 is less than the threshold value b 2  at the decision diamond  116 , meaning that the classification quantity C2 is between the threshold values b 1  and b 2 , the algorithm determines that the passenger is a class 2 passenger at box  118  and reconfigures the vehicle safety systems using basic optimal design 2 at box  120 . 
     If the classification quantity C2 is not less than the threshold value b 2  at the decision diamond  116 , then the algorithm calculates the classification quantity C3 using equation (6) at box  122 , where C3=−m 3 x+y, and determines whether the classification quantity C3 is less than the threshold value b 3  at decision diamond  124 . If the classification quantity C3 is less than the value b 3  at the decision diamond  104 , meaning that the classification quantity C3 is between the threshold values b 2  and b 3 , then the algorithm determines that the passenger is a class 3 passenger at box  126  and reconfigures the vehicle safety systems using basic optimal design 3 at box  128 . 
     If the algorithm determines that the classification quantity C3 is not less than the threshold value b 3  at the decision diamond  124 , then the algorithm calculates the classification quantity C4 using equation (7) at box  130 , where C4=−m 4 x+y, and determines whether the classification quantity C4 is less than the threshold value b 4  at decision diamond  132 . If the classification quantity C4 is less than the threshold value b 4  at the decision diamond  132 , meaning the classification quantity C4 is between the threshold values b 3  and b 4 , then the algorithm determines that the passenger is a class 4 passenger at box  134  and reconfigures the vehicle safety systems using basic optimal design 4 at box  136 . 
     If the algorithm determines that the classification quantity C4 is not less than the threshold value b 4  at the decision diamond  132 , then the algorithm calculates the classification quantity C5 using equation (8) at box  138 , where C5=−m 5 x+y, and determines whether the classification quantity C5 is less than the threshold value b 5  at decision diamond  140 . If the classification quantity C5 is less than the threshold value b 5  at the decision diamond  140 , meaning the classification quantity C4 is between the threshold values b 3  and b 4 , then the algorithm determines that the passenger is a class 5 passenger at box  142  and reconfigures the vehicle safety systems using basic optimal design 5 at box  144 . 
     If the algorithm determines that the classification quantity C5 is not less than the threshold value b 5  at the decision diamond  140 , then the algorithm calculates the classification quantity C6 using equation (9) at box  146 , where C6=−m 6 x+y, and determines whether the classification quantity C6 is less than the threshold value b 6  at decision diamond  148 . If the classification quantity C6 is less than the threshold value b 6 , meaning that the classification quantity C6 is between the threshold values b 5  and b 6 , the algorithm determines that the passenger is a class 6 passenger at box  150  and sets the vehicle safety systems using basic optimal design 6 at box  152 . 
     If the classification quantity C6 is not less than the threshold value b 6  at the decision diamond  148 , then the algorithm determines that the passenger is a class 7 passenger at box  154  and sets the vehicle safety systems using design 7 at box  156 . 
     The foregoing discussion discloses and describes merely exemplary embodiments of the present invention. One skilled in the art will readily recognize from such discussion and from the accompanying drawings and claims that various changes, modifications and variations can be made therein without departing from the spirit and scope of the invention as defined in the following claims.