Abstract:
The collision of a vehicle with a pedestrian is detected based on the response of bend sensor segments affixed to a vehicle body panel such as a bumper fascia. The sensor data is processed to identify the location of an object impacting the body panel, and is correlated with calibration data to determine the shape and mass of the object. Impacts with pedestrians are discriminated from impacts with other objects based on the determined shape and mass.

Description:
TECHNICAL FIELD 
   The present invention relates to pedestrian impact detection for a vehicle, and more particularly to a sensing method that provides timely and reliable detection of pedestrian impacts for which pedestrian safety devices should be deployed. 
   BACKGROUND OF THE INVENTION 
   A vehicle can be equipped with deployable safety devices designed to reduce injury to a pedestrian struck by the vehicle. For example, the vehicle may be equipped with one or more pedestrian air bags and/or a device for changing the inclination angle of the hood. Since these devices are only to be deployed in the event of a pedestrian impact, the deployment system must be capable of reliably distinguishing pedestrian impacts from impacts with other objects. However, equipping a production vehicle with the required sensors can be both costly and difficult. Accordingly, what is needed is a way of detecting pedestrian impacts that is more practical and cost-effective. 
   SUMMARY OF THE INVENTION 
   The present invention provides an improved method and apparatus for detecting pedestrian impacts with a vehicle. Bend sensor segments are affixed to a vehicle body panel such as a bumper fascia and are responsive to deflection of the body panel due to impacts. The sensor data is processed to identify the location of an object impacting the body panel, and is correlated with calibration data to determine the shape and mass of the object. Impacts with pedestrians are discriminated from impacts with other objects based on the determined shape and mass. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a diagram of a vehicle equipped with pedestrian safety devices, segmented bend sensors and a programmed microprocessor-based electronic control unit (ECU); 
       FIG. 2  is a block diagram depicting the functionality of the ECU of  FIG. 1 ; 
       FIG. 3A  is a diagram depicting calibration data acquired by the ECU of  FIG. 1  according to this invention; 
       FIG. 3B  is a diagram depicting a calibration data set of  FIG. 3A ; 
       FIG. 4  is a flow diagram representative of a software routine executed by the ECU of  FIG. 1  for acquiring the calibration data depicted in  FIG. 3A ; and 
       FIG. 5  is a flow diagram representative of a software routine executed by the ECU of  FIG. 1  for processing the bend sensor data to discriminate impact type. 
   

   DESCRIPTION OF THE PREFERRED EMBODIMENT 
   Referring to  FIG. 1 , the reference numeral  10  designates a vehicle that is equipped with one or more pedestrian safety devices and a sensing system for deploying the safety devices when a pedestrian impact is detected. The pedestrian safety devices (PSDs) are designated by a single block  12 , and may include one or more pedestrian air bags and a mechanism for changing the inclination angle of the vehicle hood. The PSDs  12  are selectively activated by a microprocessor-based electronic control unit (ECU)  16 , which issues a deployment command to PSD  12  on line  18  when a pedestrian impact is detected. The ECU  16  detects pedestrian impacts based on inputs from a number of sensors, including a set of bend sensors  20   a ,  20   b ,  20   c ,  20   d ,  20   e  and a vehicle speed sensor  22  (which may be responsive to wheel speed, for example). Bend sensors  20   a - 20   e  (also known as flex sensors) are deflectable strip devices having an electrical resistance that varies in relation to the amount of their deflection. Suitable bend sensors are available from Flexpoint Sensor Systems, Inc., for example. In the illustrated embodiment, bend sensors  20   a - 20   e  are mounted on the inner surface of the front bumper fascia  24  to detect frontal pedestrian impacts. A similar set of bend sensors could additionally be mounted on the rear bumper fascia  26  or any other body panel that deflects on impact. 
   The block diagram of  FIG. 2  illustrates functional elements of the ECU  16 , including an archival memory  30  for storing calibration data, a correlation unit  32  and a discrimination unit  34 . The calibration data stored in memory block  30  of  FIG. 2  is obtained by collecting bend sensor data produced when the vehicle  10  collides with various test objects at various speeds. The test objects have different masses and different shapes such as round, flat and pointed. In general, the change in output signal level (i.e., the response) of the bend sensors  20   a - 20   e  increases with increasing object mass and impact speed, and the relationship among the sensor outputs varies with object shape. An impact is detected when the response of one or more of the bend sensors  20   a - 20   e  exceeds a threshold, and the correlation unit  32  determines the impact location according to the bend sensor segment  20   a - 20   e  having the highest response. The correlation unit  32  records the vehicle speed at the time of impact and characterizes segment-to-segment differences among the bend sensors  20   a - 20   e . By correlating this data with the calibration data of memory  30 , the correlation unit  32  additionally determines the object mass and shape data. The impact location, object mass and object shape are provided as inputs to discrimination unit  34 , which determines if a pedestrian impact has occurred. In the event of a pedestrian impact, the discrimination unit  34  commands deployment of one or more PSDs  12  via line  18 . 
   The calibration data stored in memory  30  is acquired during a series of controlled impacts at the various sensor locations along bumper fascia  24 , with different test objects, and at different speeds. For each impact, two types of bend sensor data are recorded: the response of the bend sensor at the location of the impact (i.e., the on-location sensor), and normalized responses of the other bend sensors (i.e., the off-location sensors). The responses of off-location sensors are normalized by dividing them by the response of the on-location sensor. Finally, the response of the on-location sensor is recorded under the various speed and object shape constraints for objects differing in mass. For example, when a test object impacts the bumper fascia  24  at the location of bend sensor  20   a , the highest response will occur at bend sensor  20   a , and the other bend sensors  20   b - 20   e  will exhibit some change in output. All of the responses are recorded, and the off-location sensor responses are normalized with respect to the response of on-location sensor  20   a . The normalized values are then stored for various combinations of vehicle speed and object shape. The mass of the object is then adjusted, and the response of the on-location sensor  20   a  for each object mass is recorded. 
     FIG. 3A  represents the stored calibration data for impacts to bend sensor  20   a  in the form of a hierarchical look-up table. Similar data structures would exist for each of the other bend sensors  20   b - 20   e . In the representation of  FIG. 3 , calibration data has been recorded at each of four different impact speeds (VS 1 , VS 2 , VS 3 , VS 4 ), for objects having three different shapes (Round, Flat, Pointed) and two different masses (M 1 , M 2 ). Of course, the number of speed, shape and mass variations can be different than shown. Normalized responses (NR) for off-location sensors (i.e, sensors  20   b - 20   e ) are stored for each combination of impact speed and object shape.  FIG. 3B  depicts a representative normalized response (NR) data set; as indicated, the responses R 20   b , R 20   c , R 20   d , R 20   e  of the off-location sensors  20   b ,  20   c ,  20   d ,  20   e  are each divided by the response R 20   a  of the on-location sensor  20   a . The response (R) of the on-location sensor  20   a  is stored for each combination of vehicle speed, object shape and object mass. 
   The process of collecting the calibration data of  FIG. 3A  is summarized by the calibration routine  50  of  FIG. 4 . First, the block  52  records the bend sensor output signals and determines baseline signal values for each of the sensors  20   a - 20   e , by calculating a moving average, for example. Then an object of specified shape and mass impacts a specified sensor location at a specified velocity (block  54 ) while the sensor signals are monitored (block  56 ). The block  58  identifies and stores the response (R) of the on-location bend sensor, and the block  60  calculates and stores a set of normalized responses (NR) for the off-location bend sensors. In each case, the response is the peak change in value of a sensor signal relative to the respective baseline signal value. The block  62  directs re-execution of the blocks  52 - 60  with respect to a different type of impact until the calibration process has been completed. 
   The flow diagram of  FIG. 5  represents a software routine periodically executed by the correlation unit  32  of ECU  16  during operation of the vehicle  10 . Initially, the block  70  is executed to determine baseline signal values for each of the sensors  20   a - 20   e  as described above in respect to block  52  of the calibration routine  50 . The blocks  72  and  74  then monitor the sensor signals and compare the sensor responses to a predetermined threshold. If the threshold is not exceeded, the block  70  updates the moving averages used to establish the baseline signal values, and block  72  continues to monitor the sensor responses. When one or more sensor responses exceed the threshold, the blocks  76 - 92  are executed to determine and output the impact location, the object shape and the object mass. The block  76  sets the impact speed equal to the current value of vehicle speed VS. The block  78  identifies the on-location sensor as the sensor having the highest response, and the block  80  records the on-location sensor response (R). Optionally, the block  80  can also record the duration of the on-location response for correlation with corresponding calibration data. Then block  82  records a data set containing the normalized off-location sensor responses (NR). 
   The blocks  84  and  86  correlate the recorded sensor data with the stored calibration data to determine the object shape. Block  84  accesses all stored off-location calibration data for the sensor identified at block  78  and the impact speed recorded at block  76 . Referring to the table representation of  FIG. 3A , it will be assumed, for example, that sensor  20   a  has been identified as the on-location sensor and that the recorded impact speed is VS 1 ; in this example, the correlation unit  32  accesses the normalized response (NR) data sets stored at  94 ,  96  and  98 . Returning to the flow diagram of  FIG. 5 , the block  86  then correlates the off-location normalized responses recorded at block  82  with the accessed calibration data sets to determine the object shape. For example, if the recorded off-location normalized responses most nearly correlate with the normalized calibration responses stored at block  94  of FIG.  3 A, the object shape is determined to be Round as signified by the table block  100 . 
   Once the object shape has been determined, the blocks  88  and  90  correlate the recorded sensor data with the stored calibration data to determine the object mass. Block  88  accesses all stored on-location calibration data for the sensor identified at block  78 , the impact speed recorded at block  76  and the object shape determined at block  86 . Referring to the table representation of  FIG. 3A , the correlation unit  32  accesses the response (R) data stored at table blocks  102  and  104  for the example given in the previous paragraph. Returning to the flow diagram of  FIG. 5 , the block  90  then correlates the on-location response recorded at block  80  with the accessed calibration data to determine the object mass. For example, if the recorded on-location sensor response most nearly correlates with the response stored at calibration table block  102  of  FIG. 3A , the object mass is determined to be M 1  as signified by the table block  106 . 
   The routine of  FIG. 5  concludes at block  92 , which outputs the impact location, the object shape and the object mass to discrimination unit  34  of  FIG. 2 . The discrimination unit  34  uses predetermined rules to determine if the object shape and mass are representative of a pedestrian, or some other object such as a trash can or a bicycle. For example, a pedestrian impact can be detected if the object shape is round (possibly a pedestrian&#39;s leg) and the object mass is about 15 Kg. In addition to determining if the object is a pedestrian, the discrimination unit  34  can determine if and how PSD deployment should be activated based on the impact speed and impact location, for example. 
   In summary, the present invention provides a practical and cost-effective method and apparatus for detecting pedestrian impacts. While the invention has been described with respect to the illustrated embodiments, it is recognized that numerous modifications and variations in addition to those mentioned herein will occur to those skilled in the art. For example, the sensor response can be based on time rate of change or time at peak level, and so on. Accordingly, it is intended that the invention not be limited to the disclosed embodiment, but that it have the full scope permitted by the language of the following claims.