Abstract:
A method for detecting a side collision, in which either temperature or pressure sensors are provided for detecting an adiabatic increase in pressure or temperature. The signals of these sensors are filtered with a low pass filter and then, as a function of a signal of a sensitivity block, checking of the signal for various criteria is initiated. Besides the pure temperature and pressure signals, these criteria include checks of the first and second derivative of the signal as a function of time.

Description:
FIELD OF THE INVENTION 
   The present invention is based on a method for detecting a collision. 
   BACKGROUND INFORMATION 
   From German Patent No. 100 57 258, a method for side crash detection using a temperature sensor is known; this sensor measures an adiabatic increase in temperature upon a deformation of a side part of a vehicle. The absolute temperature increase and the temperature gradient are ascertained. They are compared with predetermined thresholds, to ascertain whether a side crash is involved, or not. Only if both thresholds are exceeded does a side crash exist, and a plausibility check is performed with an acceleration sensor. As a function of the plausibility check and the collision signal, restraint means are deployed as appropriate. 
   From European Published Patent Application No. 667 822, a pressure sensor is known which detects an adiabatic pressure increase in a largely closed side part of the vehicle body as a collision evaluation parameter. Once again a plausibility sensor is provided. Filtering of the signal below one kilohertz may be provided. 
   From German Patent No. 198 30 835, a method for deploying a restraint means is known in which a sensor system furnishes a pressure signal. The algorithm that detects the collision uses a variable threshold, which depends on the change in the pressure signal. 
   From German Patent No. 196 19 468, a method for deploying a restraint means for side crash protection in a vehicle is known in which the deployment of a restraint means is performed as a function of a mean value signal of the pressure signal and as a function of an increase in the pressure signal. 
   A disadvantage of the algorithms proposed in the related art for evaluating pressure and temperature signals is that there are some instances that do not call for deployment that are difficult to distinguish from genuine crashes. Especially the impact of a soccer ball, kicks, or a bicycle, or at least shutting doors very forcefully, generates pressure and temperature signals that in general are hard to distinguish from crashing into a pole at low speed. 
   SUMMARY OF THE INVENTION 
   The method according to the present invention for detecting a collision has the advantage over the above that the pressure and temperature signals are first subjected to low-pass filtration at approximately 400 Hz, since the crash-relevant data in the pressure and temperature signal are located in the low-frequency component of the signal. The low-pass filtration is moreover indispensable for attuning the sensitivity of the derivative of the signal to the scaling of the signal properties of interest. In particular, the first comparison defines the sensitivity of the algorithm. The threshold is adjusted such that exceeding the amount of the threshold, in either the positive or the negative direction, causes starting of the algorithm and further comparisons. The other comparisons may be of various kinds; the simplest comparison is the pressure or temperature signal itself. This will lead for instance to a deployment or detection of a collision, if what is involved is a high-speed crash into a hard obstacle, or an oblique crash in which the door is affected only late in the event. If the pressure change or temperature change is used, then high-speed pole crashes, that is, crashes which only locally penetrate the vehicle relatively unhindered, are detected. In a third path, it is provided that a product of the first and the delayed second derivative be compared with a threshold. Both the first and second derivatives must be greater than zero. The goal of this path is to detect a major positive curvature followed by a sharp increase. One possibility is the variant shown; others are conceivable. 
   By the combination of a low-pass filter and the use of a derivative operator once or twice, so-called wavelet filtrations with one or two vanishing moments, respectively, are created. In approximate terms, a wavelet with one vanishing moment detects changes in the signal at a defined scaling, while a wavelet with two vanishing moments more likely detects curvatures in the signal. This kind of signal course, that is, a positive curvature followed by a major increase, occurs when the penetrating object strikes more-rigid structures, such as a B pillar or a reinforcing tube, and as a result the intrusion or in other words penetration into the vehicle is braked somewhat. If the applicable structure then yields, the object penetrates all the faster. This effect may also be caused by a deformation of the penetrating object, in this case a soft barrier. In so-called misuses, or in other words mistaken deployment events caused by a soccer ball, a kick or a bicycle, such effects occur far less often, so this provides a very good criterion to distinguish between critical misuses and, for instance, low-speed crashes into a pole and soft barrier crashes. Distinguishing between misuses and crashes that do call for deployment must be done via the algorithm, since in the so-called misuses, even plausibility sensors would be enabled. Thus once the sensitivity check has found that the sensitivity threshold has been exceeded, only one of these paths needs to deploy. That is, only either the signal itself, the first derivative, or the second derivative multiplied by the first derivative. 
   It is especially advantageous that the first or at least the one second threshold is adapted in its course over time. That is, as a function of the pressure or temperature signal or their derivatives, the thresholds may be adapted in order to make it possible to react appropriately to certain situations. For instance, if a situation that leads to a pressure or temperature increase is detected and would thus cause mistaken deployment, it is possible in such a case to raise the thresholds for a certain time and then lower them again. The method of the present invention is especially well suited to detecting a side crash. If the method of the present invention is used for detecting side crashes, a frontal crash signal may optionally have an influence on the sensitivity. Thus in a crash, it is possible to prevent a frontal crash from causing unintentional deployment of side protective devices. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block circuit diagram showing an apparatus that employs the method of the present invention. 
       FIG. 2  is a block diagram of the method of the present invention for evaluating pressure sensors. 
       FIG. 3  is a block diagram of the method of the present invention for evaluating temperature signals. 
       FIG. 4  shows a typical signal course. 
   

   DETAILED DESCRIPTION 
     FIG. 1 , in a block circuit diagram, shows an apparatus in which the method of the present invention is performed. Two crash sensors  1  and  2 , which evaluate the adiabatic change of state, are each connected to a control unit  3 . A plausibility sensor  4  is also connected to control unit  3  via a third data input. Via a data output, control unit  3  is connected to a restraint system. Optionally, control unit  3  may be connected to a passenger classification system as well, so that only those restraint means that are suitable for the applicable passengers are deployed. 
   Crash sensors  1  and  2 , which furnish a signal as a function of the adiabatic change of state in a vehicle part, are either pressure sensors or temperature sensors. These sensors are disposed in a part of the vehicle that is for the most part closed, so that an adiabatic pressure increase may occur upon a deformation of the vehicle part. This type of crash sensing is extremely fast and is for instance superior to the signals of acceleration sensors. Sensors  1  and  2  include a signal processor, an analog/digital converter, and a transmitter module, which transmits the signals to control unit  3 . Sensors  1  and  2  are in fact located externally to control unit  3 , because of their function. If the two sensors  1  and  2  function as side crash sensors, then they are located in side parts of the vehicle, such as the doors. Instead of two side crash sensors, it is also possible to use more than two, for instance four, for each side part. In addition, such sensors may be installed for rear-end and frontal crashes as well. The sensor signals are then evaluated in control unit  3 , which includes a processor for the purpose. However, for deploying restraint means  5 , such as air bags or belt tighteners, the signals of sensors  1  and  2  must be checked for plausibility by a further sensor  4 . An acceleration sensor is used for this purpose. Alternatively, it is possible to use a structure-borne sound sensor or a surrounding-area sensor system for this purpose. Sensor  4  likewise includes a signal processor, an analog/digital converter, and a transmitter module, for transmitting its measurement signals to control unit  3 . Sensors  1 ,  2  and  4  may each be embodied micromechanically, since that is a production technique that makes high-precision mass production in great numbers possible. It is possible for sensor  4  to be disposed in control unit  3 , specifically in the same housing. External sensors  1 ,  2  and  4  are connected here with control unit  3  via a unidirectional connection; that is, sensors  1 ,  2  and  4  each send their data to the control unit, which then evaluates these data. However, control unit  3  sends no data to sensors  1 ,  2  and  4 . For that purpose, control unit  3  applies a direct current to the lines, and from this current, sensors  1 ,  2  and  4  each draw their energy. By current modulation, such as amplitude or pulse width modulation, the sensor values are then modulated to this direct current. Alternatively, it is possible to use a bidirectional connection in each case, or a bus to which the sensors are connected. 
     FIG. 2 , in a block diagram, shows the method of the present invention for evaluating pressure signals. Pressure signal P is fed into a low-pass filter  201 , since the crash-relevant data in the pressure signal are located in the low-frequency component of the signal. In this first step, a low-pass filtration to approximately 400 Hz is performed. The low-pass filter is preferably a third order low-pass filter, for the sake of attaining the appropriate precision. Since first and second derivatives are used in this algorithm or method, a low-pass filtration is indispensable, for adapting the sensitivity of the derivative to the scaling of the signal properties of interest over time. 
   The signal of low-pass filter  201  then passes to a sensitivity block  202 , a threshold comparator  203  for the filtered pressure signal, a further threshold comparator  204 , which compares the time derivative of the pressure with a threshold, and a third threshold comparator  205 , which compares the second derivative of the pressure, multiplied by the first derivative of the pressure, with a further threshold. Blocks  204  and  205  each include differentiator functions, for performing a time derivative of the pressure and a second time derivative of the pressure, respectively. Blocks  202  through  205  include time-dependent thresholds, with which the signals are compared. The thresholds vary as a function of the signals themselves. In this context, it is taken into account that certain given physical conditions in a certain signal behavior must be considered in order to avoid mistaken deployment events. In sensitivity block  202 , pressure signal P must have exceeded a time-dependent threshold for blocks  203  through  205  to become active. The time-dependent thresholds used rise after the algorithm has been started and may also drop again over the later course. This is appropriate, since especially in crashes that do not initially affect the door and in low-speed crashes, the door deformation takes somewhat longer, and thus because the door is always intrinsically permeable, the signal does not attain the pressure change corresponding to the volume change. 
   In block  203 , a simple time-dependent threshold is applied to the pressure signal itself and serves among other purposes for deployment in high-speed crashes into a hard obstacle and also in oblique crashes in which the door is affected only relatively late, and in which deployment of the air bag, while not absolutely necessary, is usually expected, since the automobile may be very heavily damaged. In general, the damage may be total. 
   In block  204 , the pressure signal, which has been filtered, is first differentiated once as a function of time. The pressure change, that is, a gradient, is thus available. In block  204 , a further time-dependent threshold is then applied to this pressure change, and this threshold also varies again as a function of the pressure change. Thus above all high-speed pole crashes are detected, that is, crashes with objects that only locally penetrate the vehicle relatively unhindered. 
   In block  205 , a time-dependent threshold is applied to the product of the first and a delayed second derivative of the pressure signal, both of which must be greater than zero. To that end, block  205  includes appropriate functions for differentiation of the pressure signal with respect to time once and twice. The intent here is to detect a major positive curvature followed by a sharp increase. A signal course of this kind occurs when the penetrating object strikes more-rigid structures, such as the B pillar or a reinforcing tube, and as a result the intrusion is braked. If the applicable structure then yields, the object penetrates all the faster. This effect may also be caused by the deformation of the penetrating object, such as soft barriers or another vehicle. In mistaken deployment events caused for instance by a soccer ball, a kick or a bicycle, such effects occur far less often, so that this provides a very good criterion for distinguishing between critical mistaken deployment events and, for instance, low-speed crashes into a pole and crashes into a soft barrier. Distinguishing between mistaken deployment events and deployment crashes must be done via the algorithm, since in the mistaken deployment events mentioned, plausibility sensors, such as sensor  4 , would also be enabled. 
   By the combination of low-pass filter  201  and the use of a derivative operator once or twice in block  205 , wavelet filtrations with one and two vanishing moments, respectively, are created. In approximate terms, a wavelet with one vanishing moment detects changes in the signal at a defined scaling, while a wavelet with two vanishing moments more likely detects curvatures in the signal. 
   Block  202  is connected to an AND gate  207 , while blocks  203  through  205  are connected to an OR gate  206 . The output of OR gate  206  is then connected to a second input of AND gate  207 . That is, only if sensitivity block  202 , in its threshold comparison, has detected that the pressure signal is above the first threshold, and if at least one of blocks  203  through  205  has detected the exceeding of a threshold, is a logical “1” present at the output of AND gate  207 , and in block  208  the deployment decision is made, taking into account the signal from the plausibility sensor as well. 
   In  FIG. 3 , a second block diagram is shown. The method of the present invention is shown here for a temperature sensor. Temperature signal T is fed to a low-pass filter  9  for the reasons given above. Once again, the crash-relevant data are in the low-frequency component, so that once again a low-pass filtration at approximately 400 Hz is performed. The same is true for the low-pass filtration, since once again the first and second derivatives are used to detect a crash. The filtered signal is then output from the output of low-pass filter  9  to blocks  11  through  14 . In block  11 , the sensitivity of the method of the present invention is defined. In block  12 , the temperature is compared with a time-dependent threshold. This threshold likewise varies as a function of the instantaneous temperature signal. In block  13 , the derivative of the temperature is in turn compared with a further threshold, which is also time-dependent. In block  14 , analogously to the pressure evaluation, a product of the first derivative of the temperature and the second derivative of the temperature, which is offset somewhat in time, is formed. Blocks  12 ,  13  and  14  are connected to data inputs of an OR gate  15 . The output of OR gate  15  leads to a first data input of an AND gate  16 , to whose second data input sensitivity block  11  is connected. In block  17 , the plausibility check and crash detection then take place. Furthermore, sensitivity block  11  receives a signal from front air bag  10 . 
   If sensitivity block  11  detects that the signal has exceeded a threshold, and in accordance with the amounts, or in other words in the positive or negative direction, blocks  12  through  14  then begin their comparisons. The fact that these comparisons begin when a negative threshold is undershot is due to the fact that this is possible only when a door is shut, because then the inside lining of the door lifts slightly from the door because of its inertia, resulting in a negative pressure, or in other words, cooling down in the inside portion of the door. The swing back causes a pressure increase, or in other words heating. This heating is more easily blanked out if it occurs somewhat later after the starting algorithm, since then the thresholds are generally adjusted so as to be less sensitive. 
   In block  11 , the sensitivity of the algorithm is accordingly determined; the temperature must have exceeded a time-dependent threshold in order for the other three blocks  12  through  14  to become active. This time-dependent threshold is influenced by an additional parameter  10 , specifically the flag that tells whether front air bag deployment has occurred. In this case, a pressure wave is created, which causes compression of the door volume and thus a temperature increase, which is recorded by the temperature sensor. As a result, the system becomes more sensitive to possible side crashes. For correct deployment in the case of a subsequent side collision as well, the time-dependent threshold is correspondingly increased in block  11  for the duration of the pressure wave. 
   The time-dependent thresholds used rise after the algorithm has been started and may also drop again over the later course. This is appropriate, since especially in crashes that do not initially affect the door and in low-speed crashes, the door deformation takes somewhat longer, and thus because the door is always intrinsically permeable, the signal does not attain the temperature change expected. 
   In block  11 , a simple time-dependent threshold is applied to the temperature signal itself and serves among other purposes for deployment in high-speed crashes into a hard obstacle and also in oblique crashes in which the door is affected only very late, and in which deployment of the air bag, while not absolutely necessary, is usually expected, since the automobile may be very heavily damaged. In general, the damage may be total. 
   In block  13 , a time-dependent threshold is applied to the temperature change. This path serves above all for deployment in high-speed pole crashes, that is, collisions with objects that only locally penetrate the vehicle relatively unhindered. 
   The next block  14  is a time-dependent threshold for the product of the first derivative and the delayed second derivative, both of which must be greater than zero. The goal of this block  14  is to detect a major positive curvature, followed by a sharp increase. By the combination of the low-pass filter and the use of a derivative operator once or twice, wavelet filtrations with one or two vanishing moments, respectively, are created. In approximate terms, a wavelet with one vanishing moment detects changes in the signal at a defined scaling, while a wavelet with two vanishing moments more likely detects curvatures in the signal. Such a signal course occurs when the penetrating object strikes rigid structures, such as the B pillar or the reinforcing tube. As a result the intrusion is braked somewhat. If the applicable structure then yields, the object penetrates all the faster. This effect may also be caused by the deformation of the penetrating object a soft barrier. In mistaken deployment events a soccer ball, a kick or a bicycle, such effects occur far less often, so this provides a very good criterion to distinguish between critical misuses, that is, mistaken deployment, low-speed crashes into a pole and into soft barriers, for instance. Distinguishing between mistaken deployment and crashes that do call for deployment must be done via the algorithm, since in the so-called mistaken deployment events, even plausibility sensors would be enabled. Blocks  12  through  14  may accordingly make a deployment decision independently of one another; this decision must then be confirmed with plausibility signals by other sensors installed in the car in order to reach the final decision. Thus if at least one of blocks  12  through  14  indicates a crash, then OR gate  15  sends a logical “1” to AND gate  15 , and sensitivity block  11  also outputs a logical “1” then, because after all, only then may blocks  12  through  14  be made active. In that case, AND gate  16  then outputs a logical “1” to block  17 , so that block  17  then performs this crash detection, as a function of the plausibility signals of sensor  4 , in order to deploy restraint means  5 . 
   In  FIG. 4 , a graph shows the dependency on time of the pressure in a side part of the vehicle. The time is plotted on abscissa  18 , and the pressure is plotted on ordinate  19 . Curve  100  is the pressure course in a 25 km/h collision with a bicycle, and curve  2  is the pressure course in a 20 km/h crash into a pole in the vicinity of the B pillar. Only with the aid of block  14  or  15  is it possible to classify these two cases correctly and to make a tripping decision in good time.