Abstract:
A driving support device includes a maximum measurement range determination unit adapted to calculate a distance to a plurality of points on a surface of an object present at surroundings of a vehicle. A distance sensor such as a laser range finder or a stereo camera that measures a distance is used in order to estimate bias of an acceleration sensor with high accuracy upon correction of the bias by use of the distance sensor such as the stereo camera or the LRF in a GPS non-receiving section. A sensor parameter estimation unit is adapted to estimate a parameter of the acceleration sensor based on a behavior of an own vehicle obtained from a vehicle translation and rotation angle calculation means adapted to count backwards the vehicle behavior based on distance variation and also based on a measurement value of the acceleration sensor.

Description:
TECHNICAL FIELD 
     The present invention relates to a system performing vehicle control and a device supporting vehicle driving. 
     BACKGROUND ART 
     With a method called dead-reckoning navigation used for vehicle position estimation, the vehicle position is estimated by sequentially adding a traveling direction and a speed of the vehicle, which are calculated based on a value of measurement by an inertial sensor such as a gyro or an acceleration sensor. With this dead-reckoning navigation, position estimation difference is gradually cumulated, and thus measurement accuracy of the gyro or the acceleration sensor is very important, which requires calibration of these inertial sensors and estimated position correction. 
     Conventionally, there has been a method of calibrating bias of the acceleration sensor, bias of the gyro, or a scale factor by using a measurement value from a sensor that directly measures a shift amount of a GPS or a vehicle (Patent Literature 1, Patent Literature 2, and Patent Literature 6). However, the sensor calibration by the GPS can no longer be used in, for example, a case where a GPS signal cannot be taken in, for example, indoors or a multi-path frequently occurring area or a case where accuracy of positioning by the GPS deteriorates. Under such circumstances, the gyro may be corrected by using, instead of the GPS, a distance sensor such as a laser range finder (LRF) or a stereo camera that measures a distance to an object at surroundings. Described in Patent Literature 3 is a method of estimating a vehicle behavior by using an LRF or a stereo camera. Moreover, described in Patent Literature 4 is a method of correcting gyro by extracting a feature point of a white line by a camera. Moreover, described in Patent Literature 5 is a technology of correcting an own vehicle position directly from an object at surroundings of a current position. 
     Using these technologies makes it possible to perform the position correction and correction of the bias of the sensor such as the gyro and the scale factor by the distance sensor such as the LRF or the stereo camera. 
     CITATION LIST 
     Patent Literatures 
     
         
         Patent Literature 1: Japanese Unexamined Patent Application Publication No. 2003-75172 
         Patent Literature 2: Japanese Unexamined Patent Application Publication No. 2008-175721 
         Patent Literature 3: Japanese Unexamined Patent Application Publication No. 2006-160116 
         Patent Literature 4: Japanese Unexamined Patent Application Publication No. 2008-82932 
         Patent Literature 5: Japanese Unexamined Patent Application Publication No. 2000-97714 
         Patent Literature 6 Japanese Unexamined Patent Application Publication No. 2009-281799 
       
    
     SUMMARY OF INVENTION 
     However, even if an attempt to correct the acceleration sensor in the same manner as the gyro correction by using the distance sensor such as the camera or the LRF is made, vehicle acceleration, inclination of vehicle body posture, etc. affect a value of the acceleration sensor, and further changes in vehicle body posture such as a pitch angle and a roll angle greatly affect measurement accuracy of the camera and the LRF, which therefore leads to failure to correct the bias of the acceleration sensor with high accuracy. 
     Technical Problem 
     In the present invention, bias of an acceleration sensor is estimated by: a distance sensor measuring a distance to an object present at surroundings of a vehicle; a vehicle translation and rotation angle calculation means adapted to obtain a vehicle behavior based on variation in the distance to the object measured by the distance sensor; and a sensor parameter estimation means adapted to estimate a parameter of a speed sensor based on the vehicle behavior obtained from the vehicle translation and rotation angle calculation means and a value of measurement by the acceleration sensor. 
     Advantageous Effects of Invention 
     Bias of an acceleration sensor can be estimated with high accuracy based on a value of a distance sensor. 
     Other objects, features, and advantages of the present invention will be clarified from description of embodiments of the invention below related to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram showing configuration of a vehicle control system provided with a driving support device according to the present invention. 
         FIG. 2  is a diagram showing data configuration of distance chronological information. 
         FIG. 3  is a flow chart of processing of determining a maximum measurement range. 
         FIG. 4  is a diagram showing data structure of distance sensor parameters. 
         FIG. 5  is a diagram illustrating the distance sensor parameters. 
         FIG. 6  is a diagram illustrating the distance sensor parameters. 
         FIG. 7  is a diagram illustrating narrowing of the maximum measurement range. 
         FIG. 8  is a diagram illustrating narrowing of the maximum measurement range. 
         FIG. 9  is a diagram illustrating narrowing of the maximum measurement range based on a road shape. 
         FIG. 10  is a diagram illustrating measurement by the distance sensor. 
         FIG. 11  is a flow chart of processing of calculating a translational distance and a rotation angle. 
         FIG. 12  is a flow chart of processing of associating feature point groups. 
         FIG. 13  is a diagram illustrating calculation of the translational distance and the rotation angle. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, the embodiments of the present invention will be described with reference to the drawings. 
       FIG. 1  shows an outline of a vehicle control system including a vehicle driving support device using the invention. From a distance sensor  102  such as an LRF or a stereo camera that measures a distance from a self position to an object, distance chronological information is inputted to a driving support device  101 .  FIG. 2  shows data structure of the distance chronological information. The distance chronological information from the distance sensor  102  includes: a measurement point number  202  as the number of points at which the distance is measured (measurement target points); and measurement target point positions  203  the number of which is equal to the measurement point number. Time provided by an internal clock in the driving support device  101 , at which they were inputted is recorded as measurement clock time  201  indicating clock time at which the distance was measured. The measurement target point position  203  is expressed by a coordinate system defined in the distance sensor  102 , and is expressed by a three-dimensional coordinate system, (x-axis position, y-axis position, and z-axis position) in the description below. Indication of this measurement target point position can also be expressed by a polar coordinate system (a distance to the measurement target point and a direction of the measurement target point), and essentially indicates the same contents. 
     Hereinafter, processing in the driving support device  101  will be described. 
     In the driving support device  101 , by a maximum measurement range determination means  110 , within a range measured by the distance sensor  102 , a maximum measurement range targeted for feature point detection is set, and from the measurement points measured by the distance sensor  102  in this range, a feature point is detected. Referring to  FIG. 3 , processing of determining the maximum measurement range in the maximum measurement range determination means  110  will be described. This maximum measurement range determination means  110  is activated every time the measurement clock time  201  of the distance chronological information is updated. 
     First, in step S 301 , the driving support device  101  acquires data of distance sensor parameters from a distance sensor parameter storage means  103 .  FIG. 4  shows data structure of the distance sensor parameters stored in this distance sensor parameter storage means  103 . Moreover,  FIGS. 5 and 6  show conceptual diagrams of the main parameters. Stored in the distance sensor parameter storage means  103  are: a parameter change flag  401  indicating presence or absence of a change, i.e., whether or not the distance sensor parameter was changed; and the other main parameters including attachment angles, attachment positions, measurement accuracy resolutions, and measurement ranges. Saved as the attachment angles of the distance sensor  102  are: a roll angle  402  and a pitch angle  403  with reference to a case where a vehicle body is oriented horizontally; and a yaw angle  404  for a wheel axis. Moreover, saved as the attachment positions of the distance sensor are: a horizontal shift amount  405  with respect to a vehicle body axis and a height  406  from the ground. Further saved for the measurement accuracy resolutions of the distance sensor to be used are values of: a depth direction  407 , a horizontal direction  408 , and a height direction  409 . Similarly saved as the measurement ranges of the distance sensor are values of: a depth direction  410 , a horizontal direction  411 , and a height direction  412 . Saved as initial values of the distance sensor parameters are: values obtained from different specifications of the distance sensor  102 , and values of the measured attachment positions and the measured angles. 
     Next, in step S 302 , based on the value of the parameter change flag  401  of the distance sensor parameters acquired from the distance sensor parameter storage means  103 , it is judged whether or not the parameter value has been changed. If the parameter value has been changed, the attachment angle of the distance sensor  102  with respect to the vehicle and the initial maximum measurement range determined based on the specified values of the distance sensor also change, and thus the processing proceeds to step S 303  in order to calculate the initial maximum measurement range again. If the parameter value has not been changed, there is no need of calculating the initial maximum measurement range again, and thus the processing proceeds to step S 305 . 
     In step S 303 , the attachment angles and the attachment positions are calculated from the distance sensor parameters acquired in step S 301 , and a range permitted for distance measurement by the target distance sensor  102  is calculated based on a value of the measurement range. The permitted measurement range can be obtained by (formula 1) below. 
     
       
         
           
             
               
                 
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     Here, “d” denotes the permitted measurement range (depth direction), “w” denotes the permitted measurement range (horizontal direction), “h” denotes the permitted measurement range (height direction), “Hv” denotes the attachment position (height from the ground), φ denotes the attachment angle (pitch angle), “D” denotes the permitted measurement range (depth direction), “W” denotes the measurement range (horizontal direction), and “H” denotes the measurement range (height direction). 
     Next, in step S 304 , based on the measurement accuracy resolutions of the distance sensor parameter acquired in step S 301 , a range in which the measurement accuracy can be kept in the permitted measurement range is calculated. This range in which the measurement accuracy can be kept is a region in which the measurement accuracy resolutions of the depth direction, the horizontal direction, and the height direction fall within threshold values of the measurement accuracy inside the range of the measurement by the distance sensor, and this region is stored as an initial maximum measurement range. The measurement accuracy resolution denotes an interval between measurement points at a point distant from the vehicle by a given distance, and has values for the depth, the horizontal direction, and the height direction, respectively. To keep the measurement accuracy, condition (interval between the measurement points) (size of an object targeted for the measurement) needs to be satisfied, and thus the maximum measurement range can be expressed as follows. 
     
       
         
           
             
               
                 
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     Here, d max , w max  and h max  respectively denote the maximum measurement range (depth direction), the maximum measurement range (horizontal direction), and the maximum measurement range (height direction), “rw”, “rh”, and “rd” respectively denote the resolution (horizontal direction), the resolution (height direction), and the resolution (depth direction) when separation by a distance L is done, and O denotes a minimum dimension of the measurement target object. 
     The maximum measurement range obtained in this manner is stored as the initial maximum measurement range. 
     Next, in step S 305 , map data of the surroundings at which the own vehicle runs is acquired from a map database  104 , and in step S 306 , it is judged whether or not there is road shape data in the map data including a road on which the own vehicle runs. Here, the road shape data is defined as, for example, curvature data or gradient data. If there is the road shape data, the processing proceeds to step S 307 , and if there is no road shape data, the initial maximum measurement range is stored as the maximum measurement range and the processing ends. 
     Next, in step S 307 , it is judged whether or not there is any point whose value of change in the road shape data within the initial maximum measurement range calculated in step S 304  is equal to or larger than a threshold value. The value of change in the road shape data is a difference between a value of the road shape data at a given point at the surroundings of the own vehicle and a value of the road shape data at a given point therebeyond. If the value of change in the road shape data within the initial maximum range is equal to or smaller than the threshold value, the initial maximum measurement range is stored as the maximum measurement range and the processing ends. Moreover, if the value of change in the road shape data is equal to or larger than the threshold value, a point at which it becomes equal to or larger than the threshold value is defined as a point of change in the road shape, distances (horizontal, height, and depth) to this point of change are stored, and the processing proceeds to step S 308 . 
     In step S 308 , the measurement range is narrowed down to the point of change in the road shape and stored as the maximum measurement range. If the object measured by the distance sensor is located at a place where the road shape changes, it is viewed differently at front and back of the point of change in the road shape with high possibility, which contributes to erroneous detection and an error. Thus, objects located before the point of change in the road shape when viewed from the own vehicle are not observed. As shown in  FIG. 9 , the measurement range obtained by narrowing the point of change in the road shape is stored as the maximum measurement range and the processing ends. 
     In the feature point detection means  111 , a feature point in the set maximum measurement range is detected. By using the distance chronological information inputted from the distance sensor  102 , based on relative positional relationship with each measurement point on the object surface at the surroundings of the vehicle measured at time t by the distance sensor  102 , a group of the measurement points as the feature points of the object (feature point group) is detected.  FIG. 10  shows a conceptual diagram for the detection of the feature point group. At given time t, measured positions of a plurality of measurement points are respectively projected onto an xy plane, a yz plane, and a zx plane. On each projected plane, by use of a least square method with the plurality of points as one unit, a vector formed by its point sequence is calculated. The plurality of points subject to the vector calculation here may be an aggregate of a predetermined number of adjacent points on each projected plane or an aggregate of points within a predetermined distance. If an angle formed by adjacent vectors is equal to or larger than a given value, it is recognized as a portion serving as the feature points, and all the point sequences forming the both vectors, as the feature point group, are stored into a feature point initial position saving means  112  together with measurement time of the measurement point. This processing is performed for all the measurement points within the maximum measurement range every time the distance from the object is measured in the distance sensor  102 . 
     Next, in a vehicle translation and rotation angle calculation means  113 , changes in translation and rotation of the vehicle are calculated. The vehicle translation and rotation angle calculation means  113  calculates the translation and the rotation angle of the vehicle by processing in a flow chart of  FIG. 11 . First, in step S 1101 , it is judged whether or not there is a feature point group before a given period of time in the feature point initial position saving means  112 . If there is none, the processing ends without the calculation of the translation and the rotation angle. 
     If there is a feature point group before the given period of time, the processing proceeds to step S 1102 , in which it is compared with the current feature point group and they are associated with each other. For the association of the feature point groups, a correlation coefficient ρ between the feature point groups between the feature point group before the given period of time and the current feature point group is calculated and the feature point groups with the highest correlation are defined as the feature point groups associated with each other. The correlation coefficient ρ is obtained by using (Formula 3) below. 
     
       
         
           
             
               
                 
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     Here, “x” denotes the current feature point group (vector), “y” denotes the feature point group (vector) before the given period of time, and E ( ) denotes an expected value. 
       FIG. 12  shows a flow chart of the processing of performing this association of the feature point groups in step S 1102 . First, in step S 1201 , the correlation coefficient ρ between the feature point groups between the feature point group before the given period of time and the current feature point group is obtained. Then in step S 1202 , it is judged whether or not a correlation coefficient is calculated with all combinations of feature point groups. If there is any combination of feature point groups for which the correlation coefficient has not yet been calculated, the processing returns to step S 1201 , and processing of obtaining the correlation coefficient for any combination of feature point groups for which the correlation coefficient has not yet been calculated is repeated. 
     Next, in step S 1203 , of the combinations of feature point groups which have not yet been associated, the combination of feature point groups with the highest correlation coefficient is selected. Next, in step S 1204 , it is judged whether or not a value of the highest correlation coefficient for the feature point groups which have been associated is larger than a threshold value. If it is equal to or smaller than the threshold value, judging that there is no other combination of feature point groups which have been associated, the processing ends. If it is equal to or larger than the threshold value, association is made for the combination of feature point groups selected in step S 1205 . Next, in step S 1206 , it is checked if all the feature point groups of at least one of the current feature point group and the feature point group before the given period of time are associated. If the feature point groups are associated for all the feature point groups of at least one of the current feature point group and the feature point group before the given period of time have been associated, judging that association is made for all the feature point groups, the processing ends. If there is any remaining feature point group, the processing returns to step S 1203 , and the combination of feature point groups with the next highest value of correlation coefficient is selected and the processing is repeated. 
     Returning to  FIG. 11 , through the aforementioned processing of associating the feature point groups, a translation distance and a rotation angle are obtained for the determined combination of feature point groups (S 1103 ). 
     Referring to  FIG. 13 , processing of obtaining the translation distance and the rotation angle will be described. First, as shown in (a) of  FIG. 13 , stored for the current feature point group and the feature point group before the given period of time which are associated is a shift amount of a median point when a median point of projection points for projection of the measurement points forming the current feature point group on respective planes is brought into agreement with a median point of the feature point group before the given period of time obtained in the same manner. Next, as shown in (b) of  FIG. 13 , the current feature point group is rotated around its median point, the rotation angle whose shape is closest to that of the feature point group before the given period of time is calculated by a least square method, and this rotation angle is stored. Next, as shown in (c) of  FIG. 13 , such a parallel shift amount that minimizes a difference between the projection points of the measurement points of the current feature point group and the feature point group before the given period of time that are rotated through the rotation angle is obtained by a least square method and added together with a shift amount of the median point to obtain a translation distance. 
     Then in step S 1104 , it is judged whether or not the aforementioned processing of obtaining the translation distance and the rotation angle has been performed for all the combinations of feature point groups. If the processing has been completed for all the combinations, the processing proceeds to step S 1105 . If the operation has not yet been completed, the processing returns to step S 1103 , and the processing of obtaining the translation distance and the rotation angle is repeated for the unprocessed combinations. 
     Next, in step S 1105 , respective average values of the translation distance and the rotation angle obtained for each combination of feature point groups are obtained, and in step S 1106 , these averages values are set as a translation distance and a rotation angle of the vehicle. 
     In the driving support device  101 , outputs are regularly received from the gyro  105  and the acceleration sensor  106 , an output value of the gyro  105  is subjected to addition and converted into rotation angle data and an output value of the acceleration sensor  106  is subjected to addition and converted into translation distance data in a sensor addition means  114 . Note that output cycles of the gyro  105  and the acceleration sensor  106  do not necessarily have to agree with an output cycle of the distance sensor  102 . 
     The output value of the acceleration sensor  106  is also affected by gravity acceleration, and thus the vehicle posture is calculated by using, for example, the same technology as that of Patent Literature 1 and the effect of the vehicle posture on the gravity acceleration is eliminated, conversion into speed data is performed, and the speed data is subjected to addition to thereby perform conversion into translation distance. 
     After the rotation angle data and the translation distance are calculated based on the output value of the sensor, in the sensor parameter estimation means  115 , parameters of the gyro  105  and the acceleration sensor  106  are estimated. The translation distance and the rotation angle of the vehicle calculated by the vehicle translation and rotation angle calculation means  113  by using output data of the distance sensor  102  can be compared with the translation distance data and the rotation angle data calculated in the sensor addition means  114  to thereby calculate a speed difference and an angle difference. The calculated speed difference and angle difference are divided by current clock time and a clock time difference obtained through the comparison between the translation distance data and the rotation angle data to calculate the acceleration difference and the angular speed difference. These differences are an acceleration difference and an angular speed difference per time. The acceleration difference is affected by noise of the acceleration sensor and bias of the acceleration sensor, and thus assuming that the noise of the acceleration sensor is white noise, the bias of the acceleration sensor  106  can be estimated by a Kalman filter. 
     As described above, distance displacement and angle displacement obtained in the sensor addition means  114  by using the bias value estimated in the sensor parameter estimation means  115  are corrected, and a position is estimated in a position estimation means  116 . The Kalman filter is used for the position estimation, and position difference covariance is simultaneously obtained. The estimated position and difference covariance are outputted from the driving support device  101  to the vehicle controller  107 . 
     The vehicle controller  107 , upon detection that the difference covariance outputted from the driving support device  101  increases and a range in which the own vehicle position is present is widening, judges that certainty of the own vehicle position deteriorates, and performs control in a manner such as to reduce a vehicle speed to an appropriate speed. 
     The present invention described above has been made for the embodiments, but the invention is not limited thereto, and it is clear to those skilled in the art that various modifications can be made according to spirits of the invention and an accompanying scope of the claims. 
     LIST OF REFERENCE SIGNS 
     
         
           101  Driving support device 
           102  Distance sensor 
           106  Acceleration sensor 
           107  Vehicle controller 
           110  Maximum measurement range determination means 
           115  Sensor parameter estimation means 
           116  Position estimation means