Abstract:
An object sensing apparatus for driver assistance systems in motor vehicles, including at least two sensor systems which measure data concerning the location and/or motion status of objects in the vicinity of the vehicle, and whose detection regions overlap one another, characterized by an error recognition device that checks the data measured by the sensor systems for absence of contradictions, and outputs an error signal upon detection of a contradiction.

Description:
FIELD OF THE INVENTION  
       [0001]     The present invention relates to an object sensing apparatus for driver assistance systems in motor vehicles, including at least two sensor systems which measure data concerning the location and/or motion status of objects in the vicinity of the vehicle, and whose detection regions overlap one another.  
       BACKGROUND INFORMATION  
       [0002]     Motor vehicles are increasingly being equipped with driver assistance systems that assist and provide support to the driver in driving the vehicle. One example of such an assistance system is a so-called adaptive cruise control (ACC) system, which automatically regulates the vehicle&#39;s speed to a desired speed selected by the driver or, if a preceding vehicle is present, adapts the speed in such a manner that a suitable distance from the preceding vehicle, monitored with the aid of a distance sensor, is maintained. Other examples of driver assistance systems are collision warning devices; automatic lane keeping systems (LKS), which detect roadway markings and automatically keep the vehicle in the center of the lane by intervening in the steering system; sensor-assisted parking aids, and the like. All these assistance systems require a sensor system with which information concerning the vehicle&#39;s vicinity may be sensed, as well as evaluation units with which that information may be suitably evaluated and interpreted.  
         [0003]     These devices are capable of detecting objects in the vehicle&#39;s vicinity, for example other vehicles and additional obstacles, and sensing data that characterize the location and, if applicable, the motion status of those objects. The sensor systems and associated evaluation units will therefore be referred to in combination as an object sensing apparatus.  
         [0004]     Examples of sensor systems that are used in such object sensing apparatuses are radar systems and their optical counterparts (so-called lidar systems), as well as stereo camera systems. With radar systems, the distance of the object along the line of sight may be measured by evaluating the transit time of the radar echo. The relative velocity of the object along the line of sight may also be measured directly by evaluating the Doppler shift of the radar echo. With a direction-sensitive radar system, for example a multi-beam radar, it is also possible to sense directional data concerning objects, for example the azimuth angle relative to a reference axis defined by the alignment of the radar sensor. With stereo camera systems, directional data and also (by parallax evaluation) distance data may be obtained. By evaluating the raw data measured directly by these sensor systems, it is possible to calculate data that indicate the distance of the object in the direction of travel, as well as the transverse offset of the object relative to the center of the roadway or relative to the instantaneous straight-ahead orientation of the vehicle.  
         [0005]     Since conventional sensor systems have their strengths and weaknesses as regards sensing of the requisite measured data, it is advisable to use several sensor systems that supplement one another.  
         [0006]     In ACC systems, it is conventional to subject the measured raw data to a plausibility evaluation in order to decide, or at least to indicate probabilities, as to whether the object sensed is a relevant obstacle or an irrelevant object, for example a sign at the side of the road. In some circumstances, an implausibility in the sensed data may also indicate a defect in the sensor system.  
         [0007]     It general, however, it is not possible with conventional object sensing apparatuses reliably to detect misalignments or other defects in the sensor systems that negatively affect the functionality of the assistance system.  
       SUMMARY  
       [0008]     According to an example embodiment of the present invention, an object sensing apparatus is provided with which it may be possible to detect defects in the sensor systems during operation more accurately and more reliably, and thus to improve the functional dependability of an assistance system.  
         [0009]     According to an example embodiment of the present invention, an error recognition device checks the data measured by the sensor systems for absence of contradictions, and outputs an error signal upon detection of a contradiction.  
         [0010]     As aspect of an example embodiment of the present invention is based on the consideration that when several sensor systems including mutually overlapping detection regions are present, it may often be the case that objects are located in the overlap region. In this situation, the sensor systems operating independently of one another furnish redundant information that makes possible error detection while the apparatus is in operation. When the participating sensor systems are operating correctly, the data furnished by them may be compatible with one another within certain error limits. If that is not the case—i.e. if the data contradict one another—it may be deduced therefrom that at least one of the participating sensor systems is defective, and an error signal is outputted. In one case, this error signal may be used to inform the driver of the malfunction by an optical or acoustic indicator and, if applicable, to initiate automatic deactivation of the assistance system. According to an example embodiment of the present invention, an automatic error correction may be performed using this error signal.  
         [0011]     An example embodiment of the present invention thus may make possible a continuous self-diagnosis of the object sensing apparatus during normal vehicle operation, and thus a substantial improvement in the driving safety of the assistance system that uses the data of the object sensing apparatus.  
         [0012]     In an example embodiment, the object sensing apparatus includes, in addition to a sensor system for the long-range region that is constituted e.g. by a 77-GHz radar system or a lidar system, a sensor system for the short-range region that has a shorter reach but also a larger angular region, so that dead angles in the short-range region may be largely eliminated. The sensor system for the short-range region may include a radar system or by a lidar system, or also by a video sensor system, for example a stereo camera system including two electronic cameras.  
         [0013]     In an example embodiment, three mutually independent sensor systems whose detection regions include a shared overlap region may be present. There exists in this case, for objects that are located in the shared overlap region, a capability not only for error detection but also for easily identifying the faulty sensor system by “majority decision,” and optionally for correcting the data, the alignment, or the calibration of the faulty system.  
         [0014]     Automatic identification of the faulty system and automatic error correction are possible in specific circumstances even in example embodiments including only two sensor systems, e.g., by a plausibility evaluation in consideration of the details of the physical measurement principles used in the participating sensor systems. For example, a relatively accurate distance measurement is possible with radar and lidar systems, whereas distance measurement using a stereo camera system may involve greater error tolerances (especially at longer distances) and may depend critically on camera alignment. In the event of a discrepancy, therefore, a fault in the stereo camera system is highly probable. Conversely, a video system may permit a relatively accurate measurement of the transverse offset of a preceding vehicle, whereas transverse offset measurement by a radar or lidar system may depend critically on the alignment of the radar or lidar system. In this case, therefore, a discrepancy is more suggestive of a defect in the radar or lidar system.  
         [0015]     In practice, the region in which the detection regions of the sensor systems overlap will be a region that is of relevance for the assistance system. In an ACC system, for example, the sensor systems for the short-range region and the long-range region may be configured so that they overlap in the distance region that corresponds to the typical safety distance from a preceding vehicle. In this case automatic error correction and an improvement in measurement accuracy may also be achieved by weighting the data furnished by the various sensor systems in accordance with their respective reliability, and then combining them to yield a final result.  
         [0016]     According to an example embodiment of the present invention, it may be provided to store the error signals furnished by the error recognition device together with the associated mutually contradictory measurement data, and thus to create error statistics that facilitate diagnosis when the object sensing apparatus is repaired or maintained.  
         [0017]     Example embodiments of the present invention will be explained in more detail below with reference to the appended Figures. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0018]      FIG. 1  schematically illustrates the detection regions of several sensor systems that are installed on a motor vehicle.  
         [0019]      FIG. 2  is a block diagram of an object sensing apparatus according to an example embodiment of the present invention.  
         [0020]      FIG. 3  illustrates the consequences of misalignments of various sensor systems. 
     
    
     DETAILED DESCRIPTION  
       [0021]      FIG. 1  illustrates, in a schematic plan view, the front part of a motor vehicle  10  that is equipped with three sensor systems operating independently of one another, namely a long-range radar  12 , a short-range radar  14 , and a video system that is constituted by two cameras  16 L and  16 R. Long-range radar  12  includes a detection region  18  having a range of, for example 150 m and a sensing angle of 15°, while short-range radar  14  includes a detection region  20  having a range of, for example, 50 m and a sensing angle of 400. Between these detection regions  18 ,  20 , which are not illustrated to scale in the drawings, there exists an overlap region  22 . The detection region of the video system constituted by cameras  16 L,  16 R, however, which together will be labeled with the reference character  16 , includes overlap region  22  (when visibility conditions are good). An object  24  that is located in this overlap region  22  may therefore be sensed by all three sensor systems.  
         [0022]     Detection region  18  of long-range radar  12  is symmetrical with respect to a reference axis  18 A that, when the radar sensor is correctly aligned, extends parallel to a main axis H which extends in the longitudinal direction through the center of vehicle  10 . Detection region  20  of short-range radar  14  is accordingly symmetrical with respect to a reference axis  20 A that is parallel to main axis H and to reference axis  18 A.  
         [0023]     Long-range radar  12  measures distance d1 to object  24  as well as the relative velocity of object  24  relative to vehicle  10 , and azimuth angle j 1  of object  24  relative to reference axis  18 A. Close-range radar  14  correspondingly measures distance d2 to object  24 , the relative velocity of object  24  along the line of sight from the radar sensor to the object, and azimuth angle j 2  of object  24  relative to reference axis  20 A.  
         [0024]     The images of object  24  acquired by cameras  16 L,  16 R are evaluated electronically in video system  16 . The evaluation software of such stereo camera systems is able to identify object  24  in the images acquired by the two cameras and to determine, based on the parallax shift, the location of object  24  in a two-dimensional coordinate system (parallel to the road surface). In this fashion, video system  16  furnishes perpendicular distance d3 of object  24  from vehicle  10  (i.e. from the baseline of cameras  16 L,  16 R), and transverse offset y3 of object  24  with respect to main axis H.  
         [0025]     The local coordinates of object  24  may thus be determined in three mutually independent manners using the three sensor systems  12 ,  14 ,  16 . The polar coordinates measured by the radar systems may be converted by a coordinate transformation into Cartesian coordinates, as constituted by the coordinate pair (d3, y3) in the example illustrated. The three coordinate sets measured independently of each other may then be compared to one another; if these coordinates contradict one another, this indicates that one of the three sensor systems is operating defectively. The faulty system may also be identified on the basis of the discrepant coordinate set.  
         [0026]     The relative velocity of the object  24  may also be determined by differentiation over time of distance d3 measured with video system  16 . Since the lines of sight from the radar sensors to object  24 , along which the relative velocities are measured using the Doppler effect, are not exactly parallel to main axis H, the three measured relative velocities will differ slightly from one another. Under the distance conditions that occur in practice, however, this discrepancy may be negligible. If necessary, it may be corrected by conversion into Cartesian coordinates, so that the measured velocity data may also be compared with and adjusted to one another.  
         [0027]      FIG. 2  is a block diagram of an object sensing apparatus that encompasses long-range radar  12 , short-range radar  14 , video system  16 , and associated evaluation units  26 ,  28 ,  30 , and furthermore an error recognition device  32  and a correction device  34 . Evaluation devices  26 ,  28 ,  30 , error recognition device  32 , and correction device  34  may be constituted by electronic circuits, by microcomputers, or also by software modules in a single microcomputer.  
         [0028]     From the raw data furnished by long-range radar  12 , evaluation unit  26  determines distances d1i, relative velocities v1i, and azimuth angles j 1   i  of all objects that are present in sensing region  18  of long-range radar  12 . Index i serves here to identify the individual objects. From the distance data and azimuth angles, evaluation unit  26  also calculates transverse offsets y 1   i  of the various objects.  
         [0029]     In similar fashion, evaluation unit  28  determines distances d2i, relative velocity v2i, azimuth angles j 2   i , and transverse offsets y 2   i  of all objects that are present in sensing region  20  of short-range radar  14 .  
         [0030]     Evaluation unit  30  firstly determines azimuth angles jLi and jRi of the objects sensed by cameras  16 L,  16 R. These azimuth angles are defined analogously to azimuth angles j 1  and j 2  in  FIG. 1 , i.e. they indicate the angle between the respective line of sight to the object and a straight line parallel to main axis H. Distances d3i, transverse offsets y 3   i , and (by differentiation of the distance data over time) relative velocities v3i are calculated on the basis of azimuth angles jLi and jRi and the known distance between cameras  16 L and  16 R.  
         [0031]     Distances d1i, d2i, and d3i determined by the three evaluation units  26 ,  28 ,  30  are conveyed to a distance module  36  of error recognition device  32 . Correspondingly, relative velocity data v1i, v2i, and v3i are conveyed to a velocity module  38 , and transverse offset data y 1   i , y 2   i , and y 3   i  to a transverse offset module  40 . An angle module  42  of error recognition device  32  evaluates azimuth angles j 1   i , j 2   i , jLi, and jRi.  
         [0032]     The various modules of error recognition device  32  are connected to one another, and have access to all the data that are conveyed to error recognition device  32  from any of the evaluation units. The data connections illustrated in the drawings refer in each case only to the data processed on a foreground basis in the relevant module.  
         [0033]     When evaluation unit  26  reports distance di1 of a sensed object (having index i) to distance module  36 , distance module  36  then first checks, on the basis of the associated transverse offset y 1   i , whether the object in question is also located in sensing region  20  of short-range radar  14  and/or in the sensing region of video system  16 . If that is the case, the distance module checks whether data for that object are also available from evaluation units  28 ,  30 . Identification of the objects is facilitated by the fact that distance module  36  may track the change over time in the distance data. For example, if an object is initially sensed only by long-range radar  12  and then enters sensing region  20  of short-range radar  14 , it may be expected that evaluation unit  28  will report the occurrence of a new object that may then be identified with the tracked object. To eliminate ambiguities, it is also possible to employ the criterion that the local coordinates transmitted by the various evaluation units for the same object are at least approximately consistent with one another.  
         [0034]     If distance data are available for a single object from several sensor systems, distance module  36  checks whether those distance data are consistent within the respective error limits. It is considered in this context that the error limits are themselves variable. For example, transverse offset data yli are relatively inaccurate for a large object distance, since long-range radar  12  has only a limited angular resolution and even slight deviations in the measured azimuth angle result in a considerable deviation in the associated transverse offset. If the distance data are consistent within the error limits, the consistent value di is transmitted to a downstream assistance system  44 , for example an ACC system. The outputted value di may be a weighted average of distance data d1i, d2i, and d3i, the weights being greater in proportion to the reliability of the data of the sensor system in question.  
         [0035]     Distance data d2i and d3i transmitted from evaluation units  28  and  30  are evaluated by distance module  36  in a manner corresponding to that for data d1i from evaluation unit  26 . Thus, for example, if an object is initially sensed only by short-range radar  14  and then migrates into sensing region  18  of long-range radar  12 , distance module  36  thus initially tracks the change in the data arriving from evaluation unit  28  and then checks whether corresponding data also arrive, at the expected point in time, from evaluation unit  26 .  
         [0036]     If the expected data from one of evaluation units  26 ,  28 ,  30  are absent, i.e. if a sensor system does not sense an object even though that object should, to judge by the data from the other systems, be located in the sensing region, distance module  36  then outputs an error signal Fdj. Index j here identifies the sensor system from which no data were obtained. Error signal Fdj thus indicates that the sensor system in question has possibly failed or is “blind.” 
         [0037]     If distance module  36  contains all the expected distance data but if those data deviate from one another by more than the error limits, error signal Fdj is once again outputted. In this case, error signal Fdj also indicates the sensor systems from which the discrepant data were obtained, and the magnitude of the discrepancy.  
         [0038]     If distance data that are consistent within the error limits are available from at least two sensor systems, distance value di may be created from those data and outputted to assistance system  44  even though an error has been identified and error signal Fdj has been generated.  
         [0039]     The manner of operation of velocity module  38  is analogous to the manner of operation (described above) of distance module  36 , except that here it is not the distance data but rather velocity data v1i, v2i, and v3i that are compared with one another, in order to create therefrom a velocity value vi that is outputted to assistance system  44 , and/or to output an error signal Fvj that indicates a discrepancy between the measured relative velocities.  
         [0040]     The manner of operation of transverse offset module  40  is also largely analogous to the manner of operation of distance module  36  and velocity module  38  as described above. In the example illustrated, however, no provision is made for output of an error signal in this case, since the transverse offset data are merely derived data that are calculated from the measured azimuth angles, so that the azimuth angle should be primarily relied upon for error recognition.  
         [0041]     Azimuth angles j 1   i , j 2   i , jLi, and jRi are accordingly compared separately in angle module  42 . In the comparison of these azimuth angles, consideration is given to the discrepancies that necessarily result, for a given object, from the object distance and the various positions of the relevant sensors or cameras on the baseline. If a discrepancy exceeding the error limits remains when these deviations have been considered, an error signal Ffk is outputted. Index k (k=1 through 4) in this case identifies camera  16 L or  16 R or the radar sensor whose azimuth angle or angles do not match the other azimuth angles. If a sufficiently reliable determination of the transverse offset is possible despite the discrepancy that has been identified, a corresponding value yi for the transverse offset is outputted from transverse offset module  40  to assistance system  44 .  
         [0042]     In the example illustrated, error signals Fdj, Fvj, and Ffk are conveyed to correction device  34 . If the error signals indicate with sufficient certainty which of the three sensor systems is responsible for the discrepancy, and if it is evident from the nature and magnitude of the identified error that the error may be corrected by a recalibration of the sensor system in question, a correction signal K is then outputted to the associated evaluation unit  26 ,  28 , or  30 . Optionally, the correction signal may also be outputted directly to long-range radar  12 , short-range radar  14 , or video system  16 .  
         [0043]     One example of a systematic error that may be corrected by recalibration is a misalignment of a radar sensor or a camera, which causes a deflection of the reference axis in question (e.g.  18 A or  20 A) and thus an incorrect measurement of the azimuth angle. In this case the calibration may be modified in the relevant evaluation unit in such a manner that the misalignment is corrected and the correct azimuth angle is once again obtained. Although the misalignment should still be remedied at the next service (since the misalignment also results in an undesirable change in the sensing region), system functionality may nevertheless be temporarily maintained by recalibrating.  
         [0044]     In the example embodiment illustrated, correction device  34  includes a statistics module  46  which stores error signals Fdj, Fvj, and Fjk that have occurred during operation of the apparatus, and thus documents the nature and magnitude of all the errors that occur. These data are then available for diagnostic purposes when the apparatus is serviced or repaired. Statistics module  46  additionally has, in the example illustrated, the function of deciding whether an error may be automatically corrected or whether an irresolvable error is present and an optical or acoustic error message F is outputted to the driver in order to inform him or her of the malfunction. Error message F is outputted, for example, when the signals obtained from error correction device  28  indicate a total failure of one of the sensor systems. The functions of statistics module  46  offer the possibility of not immediately outputting error message F in the case of a discrepancy that occurs only once or sporadically, but outputting the error message only when discrepancies of the same kind occur with a certain frequency. The robustness of the apparatus may thereby be considerably improved.  
         [0045]      FIG. 3  illustrates, using examples, the effect of a sensor misalignment on the measurement result.  
         [0046]     If main axis  18 A is deflected through angle Dj, for example as a result of a misalignment of long-range radar  12 , the measured azimuth angle j 1  is too great by an amount equal to that angle, and long-range radar  12  “sees” object  24  not in its actual position, but in position  24 ′ drawn with dashed lines. This results in an error Dyl in the measured transverse offset. The farther away object  24  is from vehicle  10 , the greater this error.  
         [0047]     If, on the other hand, left camera  16 L of the video system has a misalignment of the same magnitude, the associated azimuth angle jL is then distorted by an amount equal to the same angular deviation Dj, as indicated in  FIG. 3  by a line of sight S drawn as a dot-dash line. Video system  16  then sees object  24  at the intersection of the lines of sight of the two cameras, i.e. in position  24 ″. It is evident that in this case the error Dy 3  measured for the transverse offset is substantially smaller. On the other hand, however, the misalignment of camera  16 L results in a considerable error Dd 3  in the distance measurement.  
         [0048]     These relationships may be utilized in the apparatus described for automatic error correction, even when only two sensor systems are present.  
         [0049]     For example, if a misalignment of long-range radar  12  exists, a comparison of the transverse offset data from long-range radar  12  and from video system  16  yields a definite discrepancy Dyl, whereas the distance data measured with the same systems are substantially consistent. From this it may be concluded that the error is attributable to a misalignment of the radar system and not to a misalignment of a camera. It is even possible to determine the misalignment quantitatively on the basis of the measured magnitude of the error, and to correct it by recalibration of the radar sensor or the associated evaluation unit  26 .  
         [0050]     If, on the other hand, a misalignment of camera  16 L exists, this is evident from a large discrepancy Dd 3  in the distance data while the transverse offset data are largely consistent. In this case the error may be corrected by recalibrating the video system.  
         [0051]     In the event of contradictory measurement results, other criteria may also be employed for the decision as to which of the participating sensor systems is defective, e.g., including cases in which data from only two sensor systems are available for the object, or in which only two sensor systems are in fact present on the vehicle.  
         [0052]     When the transverse offset of object  24  with respect to reference axis  18 A is not zero, for example as in  FIG. 3 , azimuth angle j 1  is approximately inversely proportional to the object distance. In this case the rate of change of azimuth angle j 1  is therefore dependent on the relative velocity of the object, which is being directly measured using the radar system. If object  24  is in fact located on main axis  18 A, however, and if the transverse offset is merely being simulated by a misalignment Dj of the sensor system, the measured (apparent) azimuth angle is then independent of the distance and the relative velocity. Correspondingly, even when a transverse offset of the object actually exists, there is a discrepancy between the measured rate of change in the azimuth angle and the rate of change predicted theoretically based on the relative velocity. From this discrepancy, a misalignment of the sensor system may be deduced.  
         [0053]     With video system  16 , the possibility additionally exists of measuring the distance-dependent change in the apparent size of object  24 . This apparent change in size is directly proportional to the relative velocity, and approximately inversely proportional to the distance. An error in the distance measurement caused by misalignment of a camera may then be detected by the fact that the apparent change in size does not match the measured change in distance.  
         [0054]     Since the nature of the object (e.g. a passenger car or a truck) may also be recognized using camera system  16 , and since the typical actual size of such objects is at least approximately known, it is also possible to check whether the object distance measured with camera system  16  is compatible with the measured apparent size of the object.  
         [0055]     If road markings are also detected using camera system  16 , this information may also be utilized for automatic error recognition and error correction. The transverse offset of the vehicle itself relative to the center of the road may be detected based on the detected road markings. It is to be expected that as a statistical average, this transverse offset has a value of zero. If the evaluation in statistics module  46  shows that the measured transverse position of the vehicle itself continuously deviates in one direction away from the center of the lane, this indicates misalignment of one or both cameras. This is applicable only if the transverse offset of an object measured with the camera system deviates in the same direction from the transverse offset measured with another sensor system.