JPWO2013121898A1 - Three-dimensional object detection device - Google Patents

Abstract

One camera 10 that is mounted on the vehicle and images the rear of the vehicle, and a three-dimensional object detection unit 33 that detects a three-dimensional object existing in the right detection area A1 or the left detection area A2 behind the vehicle based on image information of the camera 10, 37, a three-dimensional object determination unit that determines whether the three-dimensional object detected by the three-dimensional object detection units 33 and 37 is another vehicle VX that exists in the right detection area A1 or the left detection area A2, and a right side behind the vehicle When the similarity calculation unit 38 calculates the similarity between the image information of the detection area A1 and the image information of the left detection area A2 behind the vehicle, and the similarity calculated by the similarity calculation unit 38 is a predetermined value or more. Includes a control unit 39 that outputs a control command to each unit so that the detected three-dimensional object is determined to be the other vehicle VX.

Description

The present invention relates to a three-dimensional object detection device.
This application claims priority based on Japanese Patent Application No. 2012-029640 filed on Feb. 14, 2012. For designated countries where reference by reference is permitted, The contents described in the application are incorporated into the present application by reference and made a part of the description of the present application.

  There is known an obstacle detection device that performs overhead conversion of an image obtained by capturing an image of the surroundings of a vehicle and detects an obstacle using a difference between two temporally converted overhead images (see Patent Document 1).

JP 2008-227646 A

  When an image of the rear of the vehicle is used to detect another vehicle traveling in the adjacent lane next to the traveling lane of the host vehicle as an obstacle, the image of the other vehicle behind the own vehicle is erroneously traveled in the adjacent lane. There is a problem of misidentifying it as an image of another vehicle.

  The problem to be solved by the present invention is to prevent erroneous detection of another vehicle directly behind the host vehicle as another vehicle traveling in an adjacent lane adjacent to the traveling lane of the own vehicle, and traveling in the adjacent lane It is to provide a three-dimensional object detection device that detects the object with high accuracy.

  The present invention calculates the similarity between the image information of the detection area set on the right side behind the host vehicle and the image information of the detection area set on the left side behind the host vehicle, and the calculated similarity is equal to or greater than a predetermined value. In this case, the above-described problem can be solved by controlling each process for determining a three-dimensional object so that it is suppressed that the detected three-dimensional object is another vehicle traveling in an adjacent lane. Solve.

  In the present invention, when the similarity between the image information in the right detection area behind the host vehicle and the image information in the left detection area is equal to or greater than a predetermined value, it is determined that there is a high possibility that another vehicle exists directly behind the host vehicle. Then, since the control is performed so that the three-dimensional object is not determined to be another vehicle traveling in the adjacent lane, the other vehicle traveling in the adjacent lane is erroneously detected based on the image of the other vehicle existing immediately behind the host vehicle. Can be prevented. As a result, it is possible to provide a three-dimensional object detection device that detects, with high accuracy, other vehicles that travel in the adjacent lane adjacent to the travel lane of the host vehicle.

1 is a schematic configuration diagram of a vehicle according to an embodiment to which a three-dimensional object detection device of the present invention is applied. It is a top view (three-dimensional object detection by difference waveform information) which shows the driving state of the vehicle of FIG. It is a block diagram which shows the detail of the computer of FIG. 4A and 4B are diagrams for explaining the outline of processing of the alignment unit in FIG. 3, in which FIG. 3A is a plan view showing a moving state of the vehicle, and FIG. It is the schematic which shows the mode of the production | generation of the difference waveform by the solid-object detection part of FIG. It is a figure which shows the small area | region divided | segmented by the solid-object detection part of FIG. It is a figure which shows an example of the histogram obtained by the solid-object detection part of FIG. It is a figure which shows the weighting by the solid-object detection part of FIG. It is a figure which shows the process by the smear detection part of FIG. 3, and the calculation process of the difference waveform by it. It is a figure which shows the other example of the histogram obtained by the solid-object detection part of FIG. FIG. 4 is a flowchart (No. 1) illustrating a three-dimensional object detection method using differential waveform information executed by the viewpoint conversion unit, the alignment unit, the smear detection unit, and the three-dimensional object detection unit of FIG. 3. FIG. 4 is a flowchart (part 2) illustrating a three-dimensional object detection method using differential waveform information executed by the viewpoint conversion unit, the alignment unit, the smear detection unit, and the three-dimensional object detection unit of FIG. 3. It is a figure (three-dimensional object detection by edge information) which shows the running state of vehicles of Drawing 1, (a) is a top view showing the positional relationship of a detection field etc., and (b) shows the positional relationship of a detection field etc. in real space. It is a perspective view shown. 4A and 4B are diagrams for explaining the operation of the luminance difference calculation unit in FIG. 3, in which FIG. 3A is a diagram illustrating a positional relationship among attention lines, reference lines, attention points, and reference points in a bird's-eye view image, and FIG. It is a figure which shows the positional relationship of the attention line, reference line, attention point, and reference point. 4A and 4B are diagrams for explaining the detailed operation of the luminance difference calculation unit in FIG. 3, in which FIG. 3A is a diagram illustrating a detection region in a bird's-eye view image, and FIG. It is a figure which shows the positional relationship of a reference point. It is a figure which shows the luminance distribution on an edge line and an edge line, (a) is a figure which shows luminance distribution when a solid object (vehicle) exists in a detection area, (b) is a solid object in a detection area It is a figure which shows the luminance distribution when not doing. FIG. 4 is a flowchart (part 1) illustrating a three-dimensional object detection method using edge information executed by a viewpoint conversion unit, a luminance difference calculation unit, an edge line detection unit, and a three-dimensional object detection unit in FIG. 3; FIG. 4 is a flowchart (part 2) illustrating a three-dimensional object detection method using edge information executed by the viewpoint conversion unit, the luminance difference calculation unit, the edge line detection unit, and the three-dimensional object detection unit of FIG. 3. It is a figure which shows the example of an image for demonstrating edge detection operation | movement. It is a figure for demonstrating the similarity with the image information of two detection areas. It is a figure for demonstrating an example of the method of calculating the similarity with the image information of two detection areas. It is a figure for demonstrating the other example of the method of calculating the similarity with the image information of two detection areas. It is a 1st figure for demonstrating an example of the method of calculating the similarity with the image information of two detection areas in turning. It is a 2nd figure for demonstrating an example of the method of calculating the similarity with the image information of two detection areas in turning. It is a figure which shows the method of masking a part of detection area. It is a 1st flowchart which shows the control procedure of the solid-object judgment in consideration of the similarity. It is a 2nd flowchart which shows the control procedure of the solid-object judgment in consideration of the similarity. It is a figure which shows an example of the relationship between the back distance from a camera, and a threshold value. It is a 3rd flowchart which shows the control procedure of the solid object judgment in consideration of the similarity. It is a 4th flowchart which shows the control procedure of the solid object judgment in consideration of the similarity.

  FIG. 1 is a schematic configuration diagram of a vehicle according to an embodiment to which a three-dimensional object detection device 1 of the present invention is applied. The three-dimensional object detection device 1 of the present example is careful when the driver of the host vehicle V is driving. Is a device that detects, as an obstacle, other vehicles that are likely to be contacted, for example, other vehicles that may be contacted when the host vehicle V changes lanes. In particular, the three-dimensional object detection device 1 of this example detects another vehicle that travels in an adjacent lane (hereinafter also simply referred to as an adjacent lane) adjacent to the lane in which the host vehicle travels. Further, the three-dimensional object detection device 1 of the present example can calculate the detected movement distance and movement speed of the other vehicle. For this reason, in the example described below, the three-dimensional object detection device 1 is mounted on the own vehicle V, and the three-dimensional object detected around the own vehicle travels in the adjacent lane next to the lane on which the own vehicle V travels. An example of detecting a vehicle will be shown. As shown in the figure, the three-dimensional object detection device 1 of the present example includes a camera 10, a vehicle speed sensor 20, a calculator 30, and a steering angle sensor 50.

  As shown in FIG. 1, the camera 10 is attached to the host vehicle V so that the optical axis is at an angle θ from the horizontal to the lower side at a height h at the rear of the host vehicle V. The camera 10 images a predetermined area in the surrounding environment of the host vehicle V from this position. In the present embodiment, there is one camera 1 provided for detecting a three-dimensional object behind the host vehicle V. However, for other purposes, for example, providing another camera for acquiring an image around the vehicle. You can also. The vehicle speed sensor 20 detects the traveling speed of the host vehicle V, and calculates the vehicle speed from the wheel speed detected by, for example, a wheel speed sensor that detects the rotational speed of the wheel. The computer 30 detects a three-dimensional object behind the vehicle, and calculates a moving distance and a moving speed for the three-dimensional object in this example. The steering angle sensor 50 detects a steering angle corresponding to the steering operation amount of the host vehicle V.

  FIG. 2 is a plan view showing a traveling state of the host vehicle V of FIG. As shown in the figure, the camera 10 images the vehicle rear side at a predetermined angle of view a. At this time, the angle of view a of the camera 10 is set to an angle of view at which the left and right lanes can be imaged in addition to the lane in which the host vehicle V travels. The area that can be imaged includes detection target areas A1 and A2 on the adjacent lane that is behind the host vehicle V and that is adjacent to the left and right of the travel lane of the host vehicle V.

  FIG. 3 is a block diagram showing details of the computer 30 of FIG. In FIG. 3, the camera 10, the vehicle speed sensor 20, and the steering angle sensor 50 are also illustrated in order to clarify the connection relationship.

  As shown in FIG. 3, the computer 30 includes a viewpoint conversion unit 31, a positioning unit 32, a three-dimensional object detection unit 33, a three-dimensional object determination unit 34, a similarity calculation unit 38, a control unit 39, and a smear. And a detection unit 40. The calculation unit 30 of the present embodiment has a configuration relating to a three-dimensional object detection block using differential waveform information. The calculation unit 30 of the present embodiment can also be configured with respect to a three-dimensional object detection block using edge information. In this case, in the configuration shown in FIG. 3, a block configuration A configured by the alignment unit 32 and the three-dimensional object detection unit 33 is surrounded by a broken line, a luminance difference calculation unit 35, an edge line detection unit 36, It can be configured by replacing the block configuration B configured by the three-dimensional object detection unit 37. Of course, both the block configuration A and the block configuration B can be provided, so that the solid object can be detected using the difference waveform information and the solid object can also be detected using the edge information. When the block configuration A and the block configuration B are provided, either the block configuration A or the block configuration B can be operated according to environmental factors such as brightness. Each configuration will be described below.

<Detection of three-dimensional object by differential waveform information>
The three-dimensional object detection device 1 according to the present embodiment detects a three-dimensional object existing in the right detection area or the left detection area behind the vehicle based on image information obtained by the monocular camera 1 that images the rear of the vehicle.

  The viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging with the camera 10 and converts the input captured image data into a bird's-eye image data in a bird's-eye view state. The state viewed from a bird's-eye view is a state viewed from the viewpoint of a virtual camera looking down from above, for example, vertically downward. This viewpoint conversion can be executed as described in, for example, Japanese Patent Application Laid-Open No. 2008-219063. The viewpoint conversion of captured image data to bird's-eye view image data is based on the principle that a vertical edge peculiar to a three-dimensional object is converted into a straight line group passing through a specific fixed point by viewpoint conversion to bird's-eye view image data. This is because a planar object and a three-dimensional object can be distinguished if used. Note that the result of the image conversion processing by the viewpoint conversion unit 31 is also used in detection of a three-dimensional object by edge information described later.

  The alignment unit 32 sequentially inputs the bird's-eye image data obtained by the viewpoint conversion of the viewpoint conversion unit 31, and aligns the positions of the inputted bird's-eye image data at different times. 4A and 4B are diagrams for explaining the outline of the processing of the alignment unit 32, where FIG. 4A is a plan view showing the moving state of the host vehicle V, and FIG. 4B is an image showing the outline of the alignment.

  As shown in FIG. 4A, it is assumed that the host vehicle V at the current time is located at V1, and the host vehicle V one hour before is located at V2. Further, the other vehicle VX is located in the rear direction of the own vehicle V and is in parallel with the own vehicle V, the other vehicle VX at the current time is located at V3, and the other vehicle VX one hour before is located at V4. Suppose you were. Furthermore, it is assumed that the host vehicle V has moved a distance d at one time. Note that “one hour before” may be a past time for a predetermined time (for example, one control cycle) from the current time, or may be a past time for an arbitrary time.

In this state, the bird's-eye image PB _t at the current time is as shown in Figure 4 (b). In the bird's-eye image PB _t, becomes a rectangular shape for the white line drawn on the road surface, but a relatively accurate is a plan view state, tilting occurs about the position of another vehicle VX at position V3. Similarly, with respect to the bird's-eye view image PB _{t-1 one} hour before, the white line drawn on the road surface is rectangular and is in a state of being relatively accurately viewed in plan, but about the other vehicle VX at the position V4. Falls down. As described above, the vertical edges of solid objects (including the edges that rise in the three-dimensional space from the road surface in addition to the vertical edges in the strict sense) are straight lines along the collapse direction by the viewpoint conversion processing to bird's-eye view image data. This is because the plane image on the road surface does not include a vertical edge, but such a fall does not occur even when the viewpoint is changed.

The alignment unit 32 performs alignment of the bird's-eye images PB _t and PB _t−1 as described above on the data. At this time, the alignment unit 32 is offset a bird's-eye view image PB _t-1 before one unit time, to match the position and bird's-eye view image PB _t at the current time. The image on the left side and the center image in FIG. 4B show a state that is offset by the movement distance d ′. This offset amount d ′ is a movement amount on the bird's-eye view image data corresponding to the actual movement distance d of the host vehicle V shown in FIG. It is determined based on the time until the time.

In addition, after the alignment, the alignment unit 32 takes the difference between the bird's-eye images PB _t and PB _t−1 and generates data of the difference image PD _t . Here, the pixel value of the difference image PD _t may be an absolute value of the difference between the pixel values of the bird's-eye images PB _t and PB _t−1 , and the absolute value is predetermined in order to cope with a change in the illumination environment. It may be set to “1” when the threshold value p is exceeded and “0” when the threshold value p is not exceeded. The image on the right side of FIG. 4B is the difference image PD _t . This threshold value p may be set in advance, or may be changed according to a control command corresponding to the similarity generated by the control unit 39 described later.

Returning to FIG. 3, the three-dimensional object detection unit 33 detects a three-dimensional object based on the data of the difference image PD _t shown in FIG. At this time, the three-dimensional object detection unit 33 of this example also calculates the movement distance of the three-dimensional object in the real space. In detecting the three-dimensional object and calculating the movement distance, the three-dimensional object detection unit 33 first generates a differential waveform. Note that the moving distance of the three-dimensional object per time is used for calculating the moving speed of the three-dimensional object. The moving speed of the three-dimensional object can be used to determine whether or not the three-dimensional object is a vehicle.

Three-dimensional object detection unit 33 of the present embodiment when generating the differential waveform sets a detection area in the difference image PD _t. The three-dimensional object detection device 1 of the present example is another vehicle that the driver of the host vehicle V pays attention to, in particular, the lane in which the host vehicle V that may be contacted when the host vehicle V changes lanes travels. Another vehicle traveling in the adjacent lane is detected as a detection target. For this reason, in this example which detects a solid object based on image information, two detection areas are set on the right side and the left side of the host vehicle V in the image obtained by the camera 1. Specifically, in the present embodiment, rectangular detection areas A1 and A2 are set on the left and right sides behind the host vehicle V as shown in FIG. The other vehicle detected in the detection areas A1 and A2 is detected as an obstacle traveling in the adjacent lane adjacent to the lane in which the host vehicle V is traveling. Such detection areas A1 and A2 may be set from a relative position with respect to the host vehicle V, or may be set based on the position of the white line. When setting the position of the white line as a reference, the movement distance detection device 1 may use, for example, an existing white line recognition technique.

  Further, the three-dimensional object detection unit 33 recognizes the sides (sides along the traveling direction) of the set detection areas A1 and A2 on the own vehicle V side as the ground lines L1 and L2 (FIG. 2). In general, the ground line means a line in which the three-dimensional object contacts the ground. However, in the present embodiment, the ground line is set as described above, not a line in contact with the ground. Even in this case, from experience, the difference between the ground line according to the present embodiment and the ground line obtained from the position of the other vehicle VX is not too large, and there is no problem in practical use.

FIG. 5 is a schematic diagram illustrating how a differential waveform is generated by the three-dimensional object detection unit 33 illustrated in FIG. 3. As shown in FIG. 5, the three-dimensional object detection unit 33 calculates a differential waveform from a portion corresponding to the detection areas A <b> 1 and A <b> 2 in the difference image PD _t (right diagram in FIG. 4B) calculated by the alignment unit 32. DW _t is generated. At this time, the three-dimensional object detection unit 33 generates a differential waveform DW _t along the direction in which the three-dimensional object falls by viewpoint conversion. In the example shown in FIG. 5, only the detection area A1 is described for convenience, but the difference waveform DW _t is generated for the detection area A2 in the same procedure.

More specifically, the three-dimensional object detection unit 33 defines a line La in the direction in which the three-dimensional object falls on the data of the difference image DW _t . Then, the three-dimensional object detection unit 33 counts the number of difference pixels DP indicating a predetermined difference on the line La. Here, the difference pixel DP indicating a predetermined difference has a predetermined threshold value when the pixel value of the difference image DW _t is an absolute value of the difference between the pixel values of the bird's-eye images PB _t and PB _t−1. If the pixel value of the difference image DW _t is expressed as “0” or “1”, the pixel indicates “1”.

  The three-dimensional object detection unit 33 counts the number of difference pixels DP, and then obtains an intersection CP between the line La and the ground line L1. Then, the three-dimensional object detection unit 33 associates the intersection CP with the count number, determines the horizontal axis position based on the position of the intersection CP, that is, the position on the vertical axis in the right diagram of FIG. The axis position, that is, the position on the right and left axis in the right diagram of FIG.

Similarly, the three-dimensional object detection unit 33 defines lines Lb, Lc... In the direction in which the three-dimensional object falls, counts the number of difference pixels DP, and determines the horizontal axis position based on the position of each intersection CP. Then, the vertical axis position is determined from the count number (number of difference pixels DP) and plotted. The three-dimensional object detection unit 33 generates the differential waveform DW _t as shown in the right diagram of FIG.

As shown in the left diagram of FIG. 5, the line La and the line Lb in the direction in which the three-dimensional object collapses have different distances overlapping the detection area A1. For this reason, if the detection area A1 is filled with the difference pixels DP, the number of difference pixels DP is larger on the line La than on the line Lb. For this reason, when the three-dimensional object detection unit 33 determines the vertical axis position from the count number of the difference pixels DP, the three-dimensional object detection unit 33 is normalized based on the distance at which the lines La and Lb in the direction in which the three-dimensional object falls and the detection area A1 overlap. Turn into. As a specific example, in the left diagram of FIG. 5, there are six difference pixels DP on the line La, and there are five difference pixels DP on the line Lb. For this reason, in determining the vertical axis position from the count number in FIG. 5, the three-dimensional object detection unit 33 normalizes the count number by dividing it by the overlap distance. Thus, as shown in the difference waveform DW _t, the line La on the direction the three-dimensional object collapses, the value of the differential waveform DW _t corresponding to Lb is substantially the same.

After the generation of the differential waveform DW _t , the three-dimensional object detection unit 33 calculates the movement distance by comparison with the differential waveform DW _t−1 one time before. That is, the three-dimensional object detection unit 33 calculates the movement distance from the time change of the difference waveforms DW _t and DW _t−1 .

More specifically, the three-dimensional object detection unit 33 divides the differential waveform DW _t into a plurality of small regions DW _{t1 to} DW _tn (n is an arbitrary integer equal to or greater than 2) as shown in FIG. FIG. 6 is a diagram illustrating the small areas DW _{t1 to} DW _tn divided by the three-dimensional object detection unit 33. The small regions DW _{t1 to} DW _tn are divided so as to overlap each other, for example, as shown in FIG. For example, the small area DW _t1 and the small area DW _t2 overlap, and the small area DW _t2 and the small area DW _t3 overlap.

Next, the three-dimensional object detection unit 33 obtains an offset amount (amount of movement of the differential waveform in the horizontal axis direction (vertical direction in FIG. 6)) for each of the small regions DW _{t1 to} DW _tn . Here, the offset amount is determined from the difference between the differential waveform DW _t in the difference waveform DW _t-1 and the current time before one unit time (distance in the horizontal axis direction). At this time, three-dimensional object detection unit 33, for each small area _DW t1 _{~DW tn,} when moving the differential waveform _{DW t1} before one unit time in the horizontal axis direction, the differential waveform DW _t at the current time The position where the error is minimized (the position in the horizontal axis direction) is determined, and the amount of movement in the horizontal axis between the original position of the differential waveform DW _t-1 and the position where the error is minimized is obtained as an offset amount. Then, the three-dimensional object detection unit 33 counts the offset amount obtained for each of the small areas DW _{t1 to} DW _tn and forms a histogram.

FIG. 7 is a diagram illustrating an example of a histogram obtained by the three-dimensional object detection unit 33. As shown in FIG. 7, the offset amount, which is the amount of movement that minimizes the error between each of the small areas DW _{t1 to} DW _tn and the differential waveform DW _t-1 one time before, has some variation. For this reason, the three-dimensional object detection unit 33 forms a histogram of offset amounts including variations, and calculates a movement distance from the histogram. At this time, the three-dimensional object detection unit 33 calculates the moving distance of the three-dimensional object from the maximum value of the histogram. That is, in the example illustrated in FIG. 7, the three-dimensional object detection unit 33 calculates the offset amount indicating the maximum value of the histogram as the movement distance τ ^* . The moving distance τ ^* is a relative moving distance of the other vehicle VX with respect to the host vehicle V. For this reason, when calculating the absolute movement distance, the three-dimensional object detection unit 33 calculates the absolute movement distance based on the obtained movement distance τ ^* and the signal from the vehicle speed sensor 20.

Incidentally, the three-dimensional object detection unit 33 Upon histogram is weighted for each of a plurality of small areas _DW t1 _{~DW tn,} the offset amount determined for each small area _DW t1 _{~DW tn} histogram of counts in response to the weight May be. FIG. 8 is a diagram illustrating weighting by the three-dimensional object detection unit 33.

As shown in FIG. 8, the small region DW _m (m is an integer of 1 to n−1) is flat. That is, in the small area DW _m , the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference is small. Three-dimensional object detection unit 33 to reduce the weight for such small area DW _m. This is because the flat small area DW _m has no characteristics and is likely to have a large error in calculating the offset amount.

On the other hand, the small area DW _{m + k} (k is an integer equal to or less than nm) is rich in undulations. That is, in the small area DW _m , the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference is large. Three-dimensional object detection unit 33 increases the weight for such small area DW _m. This is because the small region DW _{m + k} rich in undulations is characteristic and there is a high possibility that the offset amount can be accurately calculated. By weighting in this way, the calculation accuracy of the movement distance can be improved.

Although dividing the differential waveform DW _t into a plurality of small areas DW _t1 ~DW _tn in the above embodiment in order to improve the calculation accuracy of the moving distance, if the calculation accuracy of the moving distance is not less required small regions DW _t1 It is not necessary to divide into ~ DW _tn . In this case, the three-dimensional object detection unit 33 calculates the movement distance from the offset amount of the differential waveform DW _t when the error between the differential waveform DW _t and the differential waveform DW _t−1 is minimized. That is, the method for obtaining the offset amount of the difference waveform DW _t in the difference waveform DW _t-1 and the current time before one unit time is not limited to the contents.

  Returning to FIG. 3, the computer 30 includes a smear detection unit 40. The smear detection unit 40 detects a smear generation region from data of a captured image obtained by imaging with the camera 10. Since smear is a whiteout phenomenon that occurs in a CCD image sensor or the like, the smear detection unit 40 may be omitted when the camera 10 using a CMOS image sensor or the like that does not generate such smear is employed.

FIG. 9 is an image diagram for explaining the processing by the smear detection unit 40 and the calculation processing of the differential waveform DW _t thereby. First, it is assumed that data of the captured image P in which the smear S exists is input to the smear detection unit 40. At this time, the smear detection unit 40 detects the smear S from the captured image P. There are various methods for detecting the smear S. For example, in the case of a general CCD (Charge-Coupled Device) camera, the smear S is generated only in the downward direction of the image from the light source. For this reason, in this embodiment, a region having a luminance value equal to or higher than a predetermined value from the lower side of the image to the upper side of the image and continuous in the vertical direction is searched, and this is identified as a smear S generation region.

In addition, the smear detection unit 40 generates smear image SP data in which the pixel value is set to “1” for the place where the smear S occurs and the other place is set to “0”. After the generation, the smear detection unit 40 transmits the data of the smear image SP to the viewpoint conversion unit 31. In addition, the viewpoint conversion unit 31 to which the data of the smear image SP is input converts the viewpoint into a state of bird's-eye view. As a result, the viewpoint conversion unit 31 generates data of the smear bird's-eye view image SB _t . After the generation, the viewpoint conversion unit 31 transmits the data of the smear bird's-eye view image SB _t to the alignment unit 33. In addition, the viewpoint conversion unit 31 transmits the data of the smear bird's-eye view image SB _{t−1 one} hour before to the alignment unit 33.

The alignment unit 32 performs alignment of the smear bird's-eye images SB _t and SB _t−1 on the data. The specific alignment is similar to the case where the alignment of the bird's-eye images PB _t and PB _t−1 is executed on the data. Further, after the alignment, the alignment unit 32 performs a logical sum on the smear S generation region of each smear bird's-eye view image SB _t , SB _t−1 . Thereby, the alignment part 32 produces | generates the data of mask image MP. After the generation, the alignment unit 32 transmits the data of the mask image MP to the three-dimensional object detection unit 33.

The three-dimensional object detection unit 33 sets the count number of the frequency distribution to zero for the portion corresponding to the smear S generation region in the mask image MP. That is, when the differential waveform DW _t as shown in FIG. 9 is generated, the three-dimensional object detection unit 33 sets the count number SC by the smear S to zero and generates a corrected differential waveform DW _t ′. Become.

  In the present embodiment, the three-dimensional object detection unit 33 obtains the moving speed of the vehicle V (camera 10), and obtains the offset amount for the stationary object from the obtained moving speed. After obtaining the offset amount of the stationary object, the three-dimensional object detection unit 33 calculates the moving distance of the three-dimensional object after ignoring the offset amount corresponding to the stationary object among the maximum values of the histogram.

  FIG. 10 is a diagram illustrating another example of the histogram obtained by the three-dimensional object detection unit 33. When a stationary object exists in addition to the other vehicle VX within the angle of view of the camera 10, two maximum values τ1 and τ2 appear in the obtained histogram. In this case, one of the two maximum values τ1, τ2 is the offset amount of the stationary object. For this reason, the three-dimensional object detection unit 33 calculates the offset amount for the stationary object from the moving speed, ignores the maximum value corresponding to the offset amount, and calculates the moving distance of the three-dimensional object using the remaining maximum value. To do.

  Even if the offset amount corresponding to the stationary object is ignored, when there are a plurality of maximum values, it is assumed that there are a plurality of other vehicles VX within the angle of view of the camera 10. However, it is very rare that a plurality of other vehicles VX exist in the detection areas A1 and A2. For this reason, the three-dimensional object detection unit 33 stops calculating the movement distance.

Next, a solid object detection procedure based on differential waveform information will be described. 11 and 12 are flowcharts showing the three-dimensional object detection procedure of this embodiment. As shown in FIG. 11, first, the computer 30 inputs data of the image P captured by the camera 10, and generates a smear image SP by the smear detector 40 (S1). Next, the viewpoint conversion unit 31 generates data of the bird's-eye view image PB _t from the data of the captured image P from the camera 10, and also generates data of the smear bird's-eye view image SB _t from the data of the smear image SP (S2).

Then, the alignment unit 33 aligns the data of the bird's-eye view image PB _{t and} the data of the bird's-eye view image PB _{t-1 one} hour ago, and the data of the smear bird's-eye view image SB _t and the smear bird's-eye view one hour ago. The data of the image SB _t-1 is aligned (S3). After this alignment, the alignment unit 33 generates data for the difference image PD _t and also generates data for the mask image MP (S4). Then, three-dimensional object detection unit 33, the data of the difference image PD _t, and a one unit time before the difference image _{PD t-1} of the data, generates a difference waveform DW _t (S5). After generating the differential waveform DW _t , the three-dimensional object detection unit 33 sets the count number corresponding to the generation area of the smear S in the differential waveform DW _t to zero, and suppresses the influence of the smear S (S6).

Thereafter, the three-dimensional object detection unit 33 determines whether or not the peak of the differential waveform DW _t is greater than or _equal to the first threshold value α (S7). The first threshold value α can be set in advance and can be changed according to the control command of the control unit 39 shown in FIG. 3, and details thereof will be described later. Here, when the peak of the difference waveform DW _t is not equal to or greater than the first threshold value α, that is, when there is almost no difference, it is considered that there is no three-dimensional object in the captured image P. For this reason, when it is determined that the peak of the differential waveform DW _t is not equal to or greater than the first threshold value α (S7: NO), the three-dimensional object detection unit 33 does not have a three-dimensional object and has another vehicle as an obstacle. It is determined not to do so (FIG. 12: S16). Then, the processes shown in FIGS. 11 and 12 are terminated.

On the other hand, when it is determined that the peak of the difference waveform DW _t is equal to or greater than the first threshold value α (S7: YES), the three-dimensional object detection unit 33 determines that a three-dimensional object exists, and sets the difference waveform DW _t to a plurality of difference waveforms DW _t . The area is divided into small areas DW _{t1 to} DW _tn (S8). Next, the three-dimensional object detection unit 33 performs weighting for each of the small regions DW _{t1 to} DW _tn (S9). Thereafter, the three-dimensional object detection unit 33 calculates an offset amount for each of the small regions DW _{t1 to} DW _tn (S10), and generates a histogram with the weights added (S11).

  Then, the three-dimensional object detection unit 33 calculates a relative movement distance that is a movement distance of the three-dimensional object with respect to the host vehicle V based on the histogram (S12). Next, the three-dimensional object detection unit 33 calculates the absolute movement speed of the three-dimensional object from the relative movement distance (S13). At this time, the three-dimensional object detection unit 33 calculates the relative movement speed by differentiating the relative movement distance with respect to time, and adds the own vehicle speed detected by the vehicle speed sensor 20 to calculate the absolute movement speed.

  Thereafter, the three-dimensional object detection unit 33 determines whether or not the absolute movement speed of the three-dimensional object is 10 km / h or more and the relative movement speed of the three-dimensional object with respect to the host vehicle V is +60 km / h or less (S14). When both are satisfied (S14: YES), the three-dimensional object detection unit 33 determines that the three-dimensional object is the other vehicle VX (S15). Then, the processes shown in FIGS. 11 and 12 are terminated. On the other hand, when either one is not satisfied (S14: NO), the three-dimensional object detection unit 33 determines that there is no other vehicle (S16). Then, the processes shown in FIGS. 11 and 12 are terminated.

  In the present embodiment, the rear side of the host vehicle V is set as the detection areas A1 and A2, and the vehicle V travels in the adjacent lane that travels next to the travel lane of the host vehicle to which attention should be paid while traveling. Emphasis is placed on detecting the vehicle VX, and in particular, whether or not there is a possibility of contact when the host vehicle V changes lanes. This is to determine whether or not there is a possibility of contact with another vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the own vehicle when the own vehicle V changes lanes. For this reason, the process of step S14 is performed. That is, assuming that the system according to this embodiment is operated on a highway, if the speed of a three-dimensional object is less than 10 km / h, even if another vehicle VX exists, Since it is located far behind the vehicle V, there are few problems. Similarly, when the relative moving speed of the three-dimensional object with respect to the own vehicle V exceeds +60 km / h (that is, when the three-dimensional object is moving at a speed higher than 60 km / h than the speed of the own vehicle V), the lane is changed. In some cases, since the vehicle is moving in front of the host vehicle V, there is little problem. For this reason, it can be said that the other vehicle VX which becomes a problem at the time of lane change is judged in step S14.

  In step S14, determining whether the absolute moving speed of the three-dimensional object is 10 km / h or more and the relative moving speed of the three-dimensional object with respect to the host vehicle V is +60 km / h or less has the following effects. For example, depending on the mounting error of the camera 10, the absolute moving speed of the stationary object may be detected to be several km / h. Therefore, by determining whether the speed is 10 km / h or more, it is possible to reduce the possibility of determining that the stationary object is the other vehicle VX. Further, depending on the noise, the relative speed of the three-dimensional object with respect to the host vehicle V may be detected at a speed exceeding +60 km / h. Therefore, the possibility of erroneous detection due to noise can be reduced by determining whether the relative speed is +60 km / h or less.

  Furthermore, instead of the processing in step S14, it may be determined that the absolute movement speed is not negative or not 0 km / h. Further, in the present embodiment, since emphasis is placed on whether or not there is a possibility of contact when the host vehicle V changes lanes, when another vehicle VX is detected in step S15, the driver of the host vehicle is notified. A warning sound may be emitted or a display corresponding to a warning may be performed by a predetermined display device.

Thus, according to the detection procedure of the three-dimensional object based on the difference waveform information of this example, the number of pixels indicating a predetermined difference is counted on the data of the difference image PD _t along the direction in which the three-dimensional object falls by viewpoint conversion. The difference waveform DW _t is generated by frequency distribution. Here, the pixel indicating the predetermined difference on the data of the difference image PD _t is a pixel that has changed in an image at a different time, in other words, a place where a three-dimensional object exists. For this reason, the difference waveform DW _t is generated by counting the number of pixels along the direction in which the three-dimensional object collapses and performing frequency distribution at the location where the three-dimensional object exists. In particular, since the number of pixels is counted along the direction in which the three-dimensional object falls, the differential waveform DW _t is generated from the information in the height direction for the three-dimensional object. Then, the moving distance of the three-dimensional object is calculated from the time change of the differential waveform DW _t including the information in the height direction. For this reason, compared with the case where only one point of movement is focused on, the detection location before the time change and the detection location after the time change are specified including information in the height direction. The same location is likely to be obtained, and the movement distance is calculated from the time change of the same location, so that the calculation accuracy of the movement distance can be improved.

In addition, the count number of the frequency distribution is set to zero for the portion corresponding to the smear S generation region in the differential waveform DW _t . As a result, the waveform portion generated by the smear S in the differential waveform DW _t is removed, and a situation in which the smear S is mistaken as a three-dimensional object can be prevented.

Further, the moving distance of the three-dimensional object is calculated from the offset amount of the differential waveform DW _t when the error of the differential waveform DW _t generated at different times is minimized. For this reason, the movement distance is calculated from the offset amount of the one-dimensional information called the waveform, and the calculation cost can be suppressed in calculating the movement distance.

Further, the differential waveform DW _t generated at different times is divided into a plurality of small regions DW _{t1 to} DW _tn . By dividing this into multiple small areas DW _t1 ~DW _tn, so that the obtained plurality of waveforms showing the respective locations of the three-dimensional object. Also, determine the offset amount when the error of each waveform for each small area _DW t1 _{~DW tn} is minimized by histogram by counting the offset amount determined for each small area _DW t1 _{~DW tn,} The moving distance of the three-dimensional object is calculated. For this reason, the offset amount is obtained for each part of the three-dimensional object, and the movement distance is obtained from a plurality of offset amounts, so that the calculation accuracy of the movement distance can be improved.

Further, the weighting for each of a plurality of small areas _DW t1 _{~DW tn,} histogram of counts in accordance with the offset amount obtained for each small area _DW t1 _{~DW tn} the weight. For this reason, the moving distance can be calculated more appropriately by increasing the weight for the characteristic area and decreasing the weight for the non-characteristic area. Therefore, the calculation accuracy of the moving distance can be further improved.

For each of the small regions DW _{t1 to} DW _tn of the differential waveform DW _t , the weight is increased as the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference is larger. For this reason, the characteristic undulation region having a large difference between the maximum value and the minimum value has a larger weight, and the flat region having a small undulation has a smaller weight. Here, since it is easier to obtain the offset amount more accurately in the shape of the undulating area than in the flat area, the moving distance is calculated by increasing the weight in the area where the difference between the maximum value and the minimum value is large. The accuracy can be further improved.

Further, the moving distance of the three-dimensional object is calculated from the maximum value of the histogram obtained by counting the offset amount obtained for each of the small areas DW _{t1 to} DW _tn . For this reason, even if there is a variation in the offset amount, a more accurate movement distance can be calculated from the maximum value.

  Further, since the offset amount for the stationary object is obtained and this offset amount is ignored, it is possible to prevent a situation in which the calculation accuracy of the moving distance of the three-dimensional object is lowered due to the stationary object. In addition, if there are a plurality of maximum values after ignoring the offset amount corresponding to the stationary object, the calculation of the moving distance of the three-dimensional object is stopped. For this reason, it is possible to prevent a situation in which an erroneous movement distance having a plurality of maximum values is calculated.

  In the above embodiment, the vehicle speed of the host vehicle V is determined based on a signal from the vehicle speed sensor 20, but the present invention is not limited to this, and the speed may be estimated from a plurality of images at different times. In this case, a vehicle speed sensor becomes unnecessary, and the configuration can be simplified.

In the above-described embodiment, the captured image at the current time and the image one hour before are converted into a bird's-eye view, the converted bird's-eye view is aligned, the difference image PD _t is generated, and the generated difference image PD _{Although t} is evaluated along the falling direction (the falling direction of the three-dimensional object when the captured image is converted into a bird's eye view), the differential waveform DW _t is generated, but the present invention is not limited to this. For example, only the image one hour before is converted into a bird's-eye view, the converted bird's-eye view is converted into an equivalent to the image captured again, a difference image is generated between this image and the current time image, and the generated difference image The differential waveform DW _t may be generated by evaluating along the direction corresponding to the falling direction (that is, the direction in which the falling direction is converted into the direction on the captured image). That is, the three-dimensional object when the image of the current time and the image of one hour before are aligned, the difference image PD _t is generated from the difference between the two images subjected to the alignment, and the difference image PD _t is converted into a bird's eye view The bird's-eye view does not necessarily have to be clearly generated as long as the evaluation can be performed along the direction in which the user falls.

《Detection of solid objects by edge information》
Next, it is possible to operate instead of the block A shown in FIG. 3 to detect a three-dimensional object using edge information configured by a luminance difference calculation unit 35, an edge line detection unit 36, and a three-dimensional object detection unit 37. The block B will be described. FIGS. 13A and 13B are diagrams illustrating an imaging range and the like of the camera 10 in FIG. 3. FIG. 13A is a plan view, and FIG. 13B is a perspective view in real space on the rear side from the vehicle V. Show. As shown in FIG. 13A, the camera 10 has a predetermined angle of view a, and images the rear side from the host vehicle V included in the predetermined angle of view a. Similarly to the case shown in FIG. 2, the angle of view “a” of the camera 10 is set so that the imaging range of the camera 10 includes the adjacent lane in addition to the lane in which the host vehicle V travels.

Detection area A1, A2 of the present embodiment is in a plan view (a state of being bird's view) a trapezoidal shape, location of the detection areas A1, A2, size and shape, based on the distance d ₁ to d ₄ determines Is done. The detection areas A1 and A2 in the example shown in the figure are not limited to a trapezoidal shape, and may be other shapes such as a rectangle when viewed from a bird's eye view as shown in FIG.

  Here, the distance d1 is a distance from the host vehicle V to the ground lines L1 and L2. The ground lines L1 and L2 mean lines on which a three-dimensional object existing in the lane adjacent to the lane in which the host vehicle V travels contacts the ground. The purpose of the present embodiment is to detect other vehicles VX and the like (including two-wheeled vehicles) traveling in the left and right lanes adjacent to the lane of the host vehicle V on the rear side of the host vehicle V. For this reason, a distance d1 which is a position to be the ground lines L1 and L2 of the other vehicle VX is obtained from a distance d11 from the own vehicle V to the white line W and a distance d12 from the white line W to a position where the other vehicle VX is predicted to travel. It can be determined substantially fixedly.

  Further, the distance d1 is not limited to being fixedly determined, and may be variable. In this case, the computer 30 recognizes the position of the white line W with respect to the host vehicle V by a technique such as white line recognition, and determines the distance d11 based on the recognized position of the white line W. Thereby, the distance d1 is variably set using the determined distance d11. In the following embodiment, since the position where the other vehicle VX travels (distance d12 from the white line W) and the position where the host vehicle V travels (distance d11 from the white line W) are roughly determined, the distance d1 is It shall be fixedly determined.

  The distance d2 is a distance extending from the rear end portion of the host vehicle V in the vehicle traveling direction. The distance d2 is determined so that the detection areas A1 and A2 are at least within the angle of view a of the camera 10. In particular, in the present embodiment, the distance d2 is set so as to be in contact with the range divided into the angle of view a. The distance d3 is a distance indicating the length of the detection areas A1, A2 in the vehicle traveling direction. This distance d3 is determined based on the size of the three-dimensional object to be detected. In the present embodiment, since the detection target is the other vehicle VX or the like, the distance d3 is set to a length including the other vehicle VX.

  As shown in FIG. 13B, the distance d4 is a distance indicating a height that is set to include a tire such as the other vehicle VX in the real space. The distance d4 is a length shown in FIG. 13A in the bird's-eye view image. The distance d4 may be a length that does not include a lane that is further adjacent to the left and right adjacent lanes in the bird's-eye view image (that is, a lane that is adjacent to two lanes). If the lane adjacent to the two lanes is included from the lane of the own vehicle V, there is another vehicle VX in the adjacent lane on the left and right of the own lane that is the lane in which the own vehicle V is traveling, or in the lane adjacent to the two lanes This is because it becomes impossible to distinguish whether there is another vehicle VX.

  As described above, the distances d1 to d4 are determined, and thereby the positions, sizes, and shapes of the detection areas A1 and A2 are determined. More specifically, the position of the upper side b1 of the detection areas A1 and A2 forming a trapezoid is determined by the distance d1. The starting point position C1 of the upper side b1 is determined by the distance d2. The end point position C2 of the upper side b1 is determined by the distance d3. The side b2 of the detection areas A1 and A2 having a trapezoidal shape is determined by a straight line L3 extending from the camera 10 toward the starting point position C1. Similarly, a side b3 of trapezoidal detection areas A1 and A2 is determined by a straight line L4 extending from the camera 10 toward the end position C2. The position of the lower side b4 of the detection areas A1 and A2 having a trapezoidal shape is determined by the distance d4. Thus, the area surrounded by the sides b1 to b4 is set as the detection areas A1 and A2. As shown in FIG. 13B, the detection areas A <b> 1 and A <b> 2 are true squares (rectangles) in the real space behind the host vehicle V.

  Returning to FIG. 3, the viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging by the camera 10. The viewpoint conversion unit 31 performs viewpoint conversion processing on the input captured image data to the bird's-eye image data in a bird's-eye view state. The bird's-eye view is a state seen from the viewpoint of a virtual camera looking down from above, for example, vertically downward (or slightly obliquely downward). This viewpoint conversion process can be realized by a technique described in, for example, Japanese Patent Application Laid-Open No. 2008-219063.

  The luminance difference calculation unit 35 calculates a luminance difference with respect to the bird's-eye view image data subjected to viewpoint conversion by the viewpoint conversion unit 31 in order to detect the edge of the three-dimensional object included in the bird's-eye view image. For each of a plurality of positions along a vertical imaginary line extending in the vertical direction in the real space, the brightness difference calculating unit 35 calculates a brightness difference between two pixels in the vicinity of each position. The luminance difference calculation unit 35 can calculate the luminance difference by either a method of setting only one vertical virtual line extending in the vertical direction in the real space or a method of setting two vertical virtual lines.

  A specific method for setting two vertical virtual lines will be described. The brightness difference calculation unit 35 applies a first vertical imaginary line corresponding to a line segment extending in the vertical direction in the real space and a vertical direction in the real space different from the first vertical imaginary line with respect to the bird's-eye view image that has undergone viewpoint conversion. A second vertical imaginary line corresponding to the extending line segment is set. The luminance difference calculation unit 35 continuously obtains a luminance difference between a point on the first vertical imaginary line and a point on the second vertical imaginary line along the first vertical imaginary line and the second vertical imaginary line. Hereinafter, the operation of the luminance difference calculation unit 35 will be described in detail.

  As shown in FIG. 14A, the luminance difference calculation unit 35 corresponds to a line segment extending in the vertical direction in the real space and passes through the detection area A1 (hereinafter referred to as the attention line La). Set). In addition, unlike the attention line La, the luminance difference calculation unit 35 corresponds to a line segment extending in the vertical direction in the real space and also passes through the second vertical virtual line Lr (hereinafter referred to as a reference line Lr) passing through the detection area A1. Set. Here, the reference line Lr is set at a position separated from the attention line La by a predetermined distance in the real space. Note that the line corresponding to the line segment extending in the vertical direction in the real space is a line that spreads radially from the position Ps of the camera 10 in the bird's-eye view image. This radially extending line is a line along the direction in which the three-dimensional object falls when converted to bird's-eye view.

  The luminance difference calculation unit 35 sets a point of interest Pa (a point on the first vertical imaginary line) on the line of interest La. In addition, the luminance difference calculation unit 35 sets a reference point Pr (a point on the second vertical plate) on the reference line Lr. The attention line La, the attention point Pa, the reference line Lr, and the reference point Pr have the relationship shown in FIG. 14B in the real space. As is clear from FIG. 14B, the attention line La and the reference line Lr are lines extending in the vertical direction in the real space, and the attention point Pa and the reference point Pr are substantially the same height in the real space. This is the point that is set. Note that the attention point Pa and the reference point Pr do not necessarily have the same height, and an error that allows the attention point Pa and the reference point Pr to be regarded as the same height is allowed.

  The luminance difference calculation unit 35 calculates a luminance difference between the attention point Pa and the reference point Pr. If the luminance difference between the attention point Pa and the reference point Pr is large, it is considered that an edge exists between the attention point Pa and the reference point Pr. Therefore, the edge line detection unit 36 shown in FIG. 3 detects an edge line based on the luminance difference between the attention point Pa and the reference point Pr.

  This point will be described in more detail. FIG. 15 is a diagram illustrating a detailed operation of the luminance difference calculation unit 35, in which FIG. 15 (a) shows a bird's-eye view image in a bird's-eye view state, and FIG. 15 (b) is shown in FIG. 15 (a). It is the figure which expanded a part B1 of the bird's-eye view image. Although only the detection area A1 is illustrated and described in FIG. 15, the luminance difference is calculated in the same procedure for the detection area A2.

  When the other vehicle VX is reflected in the captured image captured by the camera 10, the other vehicle VX appears in the detection area A1 in the bird's-eye view image as shown in FIG. As shown in the enlarged view of the area B1 in FIG. 15A in FIG. 15B, it is assumed that the attention line La is set on the rubber part of the tire of the other vehicle VX on the bird's-eye view image. In this state, the luminance difference calculation unit 35 first sets the reference line Lr. The reference line Lr is set along the vertical direction at a position away from the attention line La by a predetermined distance in the real space. Specifically, in the three-dimensional object detection device 1 according to the present embodiment, the reference line Lr is set at a position separated from the attention line La by 10 cm in real space. Thereby, the reference line Lr is set on the wheel of the tire of the other vehicle VX that is separated from the rubber of the tire of the other vehicle VX by, for example, 10 cm on the bird's eye view image.

  Next, the luminance difference calculation unit 35 sets a plurality of attention points Pa1 to PaN on the attention line La. In FIG. 15B, for the convenience of explanation, six attention points Pa1 to Pa6 (hereinafter simply referred to as attention points Pai when showing arbitrary points) are set. Note that the number of attention points Pa set on the attention line La may be arbitrary. In the following description, it is assumed that N attention points Pa are set on the attention line La.

  Next, the luminance difference calculation unit 35 sets the reference points Pr1 to PrN so as to be the same height as the attention points Pa1 to PaN in the real space. Then, the luminance difference calculation unit 35 calculates the luminance difference between the attention point Pa and the reference point Pr having the same height. Thereby, the brightness | luminance difference calculation part 35 calculates the brightness | luminance difference of two pixels for every some position (1-N) along the vertical imaginary line extended in the perpendicular direction in real space. For example, the luminance difference calculating unit 35 calculates a luminance difference between the first attention point Pa1 and the first reference point Pr1, and the second difference between the second attention point Pa2 and the second reference point Pr2. Will be calculated. Thereby, the luminance difference calculation unit 35 continuously calculates the luminance difference along the attention line La and the reference line Lr. That is, the luminance difference calculation unit 35 sequentially obtains the luminance difference between the third to Nth attention points Pa3 to PaN and the third to Nth reference points Pr3 to PrN.

  The luminance difference calculation unit 35 repeatedly executes the above-described processing such as setting the reference line Lr, setting the attention point Pa and the reference point Pr, and calculating the luminance difference while shifting the attention line La in the detection area A1. That is, the luminance difference calculation unit 35 repeatedly executes the above processing while changing the positions of the attention line La and the reference line Lr by the same distance in the extending direction of the ground line L1 in the real space. For example, the luminance difference calculation unit 35 sets the reference line Lr as the reference line Lr in the previous processing, sets the reference line Lr for the attention line La, and sequentially obtains the luminance difference. It will be.

  Returning to FIG. 3, the edge line detection unit 36 detects an edge line from the continuous luminance difference calculated by the luminance difference calculation unit 35. For example, in the case illustrated in FIG. 15B, the first attention point Pa <b> 1 and the first reference point Pr <b> 1 are located in the same tire portion, and thus the luminance difference is small. On the other hand, the second to sixth attention points Pa2 to Pa6 are located in the rubber part of the tire, and the second to sixth reference points Pr2 to Pr6 are located in the wheel part of the tire. Therefore, the luminance difference between the second to sixth attention points Pa2 to Pa6 and the second to sixth reference points Pr2 to Pr6 increases. For this reason, the edge line detection unit 36 may detect that an edge line exists between the second to sixth attention points Pa2 to Pa6 and the second to sixth reference points Pr2 to Pr6 having a large luminance difference. it can.

Specifically, when detecting the edge line, the edge line detection unit 36 firstly follows the following Equation 1 to determine the i-th attention point Pai (coordinate (xi, yi)) and the i-th reference point Pri (coordinate ( xi ′, yi ′)) and the i th attention point Pai are attributed.
[Equation 1]
When I (xi, yi)> I (xi ′, yi ′) + t s (xi, yi) = 1
When I (xi, yi) <I (xi ′, yi ′) − t s (xi, yi) = − 1
Otherwise s (xi, yi) = 0

  In Equation 1, t represents a threshold value, I (xi, yi) represents the luminance value of the i-th attention point Pai, and I (xi ′, yi ′) represents the luminance value of the i-th reference point Pri. . According to Equation 1, when the luminance value of the attention point Pai is higher than the luminance value obtained by adding the threshold value t to the reference point Pri, the attribute s (xi, yi) of the attention point Pai is “1”. Become. On the other hand, when the luminance value of the attention point Pai is lower than the luminance value obtained by subtracting the threshold value t from the reference point Pri, the attribute s (xi, yi) of the attention point Pai is “−1”. When the luminance value of the attention point Pai and the luminance value of the reference point Pri are in other relationships, the attribute s (xi, yi) of the attention point Pai is “0”. The threshold value t can be set in advance and can be changed in accordance with a control command issued by the control unit 39 shown in FIG. 3, and details thereof will be described later.

Next, the edge line detection unit 36 determines whether or not the attention line La is an edge line from the continuity c (xi, yi) of the attribute s along the attention line La based on Equation 2 below.
[Equation 2]
When s (xi, yi) = s (xi + 1, yi + 1) (and excluding 0 = 0),
c (xi, yi) = 1
Other than the above
c (xi, yi) = 0

  When the attribute s (xi, yi) of the attention point Pai and the attribute s (xi + 1, yi + 1) of the adjacent attention point Pai + 1 are the same, the continuity c (xi, yi) is ‘1’. When the attribute s (xi, yi) of the attention point Pai is not the same as the attribute s (xi + 1, yi + 1) of the adjacent attention point Pai + 1, the continuity c (xi, yi) is “0”.

  Next, the edge line detection unit 36 obtains the sum for the continuity c of all attention points Pa on the attention line La. The edge line detection unit 36 normalizes the continuity c by dividing the obtained sum of continuity c by the number N of points of interest Pa. The edge line detection unit 36 determines that the attention line La is an edge line when the normalized value exceeds the threshold θ. The threshold value θ is a value set in advance through experiments or the like. The threshold value θ may be set in advance or may be changed according to a control command corresponding to the similarity of the control unit 39 described later.

That is, the edge line detection unit 36 determines whether or not the attention line La is an edge line based on Equation 3 below. Then, the edge line detection unit 36 determines whether or not all the attention lines La drawn on the detection area A1 are edge lines.
[Equation 3]
Σc (xi, yi) / N> θ

  Returning to FIG. 3, the three-dimensional object detection unit 37 detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 36. As described above, the three-dimensional object detection device 1 according to the present embodiment detects an edge line extending in the vertical direction in real space. The fact that many edge lines extending in the vertical direction are detected means that there is a high possibility that a three-dimensional object exists in the detection areas A1 and A2. For this reason, the three-dimensional object detection unit 37 detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 36. Furthermore, prior to detecting the three-dimensional object, the three-dimensional object detection unit 37 determines whether or not the edge line detected by the edge line detection unit 36 is correct. The three-dimensional object detection unit 37 determines whether or not the luminance change along the edge line of the bird's-eye view image on the edge line is larger than a predetermined threshold value. When the luminance change of the bird's-eye view image on the edge line is larger than the threshold value, it is determined that the edge line is detected by erroneous determination. On the other hand, when the luminance change of the bird's-eye view image on the edge line is not larger than the threshold value, it is determined that the edge line is correct. This threshold value is a value set in advance by experiments or the like.

  FIG. 16 is a diagram illustrating the luminance distribution of the edge line. FIG. 16A illustrates the edge line and the luminance distribution when another vehicle VX as a three-dimensional object exists in the detection area A1, and FIG. Indicates an edge line and a luminance distribution when there is no solid object in the detection area A1.

  As shown in FIG. 16A, it is assumed that the attention line La set in the tire rubber portion of the other vehicle VX is determined to be an edge line in the bird's-eye view image. In this case, the luminance change of the bird's-eye view image on the attention line La is gentle. This is because the tire of the other vehicle VX is extended in the bird's-eye view image by converting the image captured by the camera 10 into a bird's-eye view image. On the other hand, as shown in FIG. 16B, it is assumed that the attention line La set in the white character portion “50” drawn on the road surface in the bird's-eye view image is erroneously determined as an edge line. In this case, the brightness change of the bird's-eye view image on the attention line La has a large undulation. This is because a portion with high brightness in white characters and a portion with low brightness such as a road surface are mixed on the edge line.

  Based on the difference in luminance distribution on the attention line La as described above, the three-dimensional object detection unit 37 determines whether or not the edge line is detected by erroneous determination. When the luminance change along the edge line is larger than a predetermined threshold, the three-dimensional object detection unit 37 determines that the edge line is detected by erroneous determination. And the said edge line is not used for the detection of a solid object. Thereby, white characters such as “50” on the road surface, weeds on the road shoulder, and the like are determined as edge lines, and the detection accuracy of the three-dimensional object is prevented from being lowered.

Specifically, the three-dimensional object detection unit 37 calculates the luminance change of the edge line by any one of the following mathematical formulas 4 and 5. The luminance change of the edge line corresponds to the evaluation value in the vertical direction in the real space. Equation 4 below evaluates the luminance distribution by the sum of the squares of the differences between the i-th luminance value I (xi, yi) on the attention line La and the adjacent i + 1-th luminance value I (xi + 1, yi + 1). . Equation 5 below evaluates the luminance distribution by the sum of the absolute values of the differences between the i-th luminance value I (xi, yi) on the attention line La and the adjacent i + 1-th luminance value I (xi + 1, yi + 1). To do.
[Equation 4]
Evaluation value in the vertical equivalent direction = Σ [{I (xi, yi) −I (xi + 1, yi + 1)} ² ]
[Equation 5]
Evaluation value in the vertical equivalent direction = Σ | I (xi, yi) −I (xi + 1, yi + 1) |

In addition, not only Formula 5 but also Formula 6 below, the threshold value t2 is used to binarize the attribute b of the adjacent luminance value, and the binarized attribute b is summed for all the attention points Pa. Also good.
[Equation 6]
Evaluation value in the vertical equivalent direction = Σb (xi, yi)
However, when | I (xi, yi) −I (xi + 1, yi + 1) |> t2,
b (xi, yi) = 1
Other than the above
b (xi, yi) = 0

  When the absolute value of the luminance difference between the luminance value of the attention point Pai and the luminance value of the reference point Pri is larger than the threshold value t2, the attribute b (xi, yi) of the attention point Pa (xi, yi) is “1”. Become. If the relationship is other than that, the attribute b (xi, yi) of the attention point Pai is '0'. This threshold value t2 is set in advance by an experiment or the like in order to determine that the attention line La is not on the same three-dimensional object. Then, the three-dimensional object detection unit 37 sums up the attributes b for all the attention points Pa on the attention line La, obtains an evaluation value in the vertical equivalent direction, and determines whether the edge line is correct.

  Next, a three-dimensional object detection method using edge information according to the present embodiment will be described. 17 and 18 are flowcharts showing details of the three-dimensional object detection method according to the present embodiment. In FIG. 17 and FIG. 18, for the sake of convenience, the processing for the detection area A1 will be described, but the same processing is executed for the detection area A2.

  As shown in FIG. 17, first, in step S21, the camera 10 captures an image of a predetermined area specified by the angle of view a and the attachment position. Next, in step S22, the viewpoint conversion unit 31 inputs the captured image data captured by the camera 10 in step S21, performs viewpoint conversion, and generates bird's-eye view image data.

  Next, the brightness | luminance difference calculation part 35 sets attention line La on detection area | region A1 in step S23. At this time, the luminance difference calculation unit 35 sets a line corresponding to a line extending in the vertical direction in the real space as the attention line La. Next, the brightness | luminance difference calculation part 35 sets the reference line Lr on detection area | region A1 in step S24. At this time, the luminance difference calculation unit 35 sets a reference line Lr that corresponds to a line segment extending in the vertical direction in the real space and is separated from the attention line La by a predetermined distance in the real space.

  Next, the brightness | luminance difference calculation part 35 sets several attention point Pa on attention line La in step S25. At this time, the luminance difference calculation unit 35 sets the attention points Pa as many as not causing a problem at the time of edge detection by the edge line detection unit 36. In step S26, the luminance difference calculation unit 35 sets the reference point Pr so that the attention point Pa and the reference point Pr are substantially the same height in the real space. Thereby, the attention point Pa and the reference point Pr are arranged in a substantially horizontal direction, and it becomes easy to detect an edge line extending in the vertical direction in the real space.

  Next, in step S27, the luminance difference calculation unit 35 calculates the luminance difference between the attention point Pa and the reference point Pr that have the same height in the real space. Next, the edge line detection unit 36 calculates the attribute s of each attention point Pa in accordance with Equation 1 above. Next, in step S28, the edge line detection unit 36 calculates the continuity c of the attribute s of each attention point Pa in accordance with Equation 2 above. Next, in step S29, the edge line detection unit 36 determines whether or not the value obtained by normalizing the total sum of continuity c is greater than the threshold value θ according to the above formula 3. When it is determined that the normalized value is larger than the threshold θ (S29: YES), the edge line detection unit 36 detects the attention line La as an edge line in step S30. Then, the process proceeds to step S31. When it is determined that the normalized value is not larger than the threshold θ (S29: NO), the edge line detection unit 36 does not detect the attention line La as an edge line, and the process proceeds to step S31. This threshold value θ can be set in advance, but can be changed in accordance with a control command to the control unit 39.

  In step S31, the computer 30 determines whether or not the processing in steps S23 to S30 has been executed for all the attention lines La that can be set on the detection area A1. If it is determined that the above processing has not been performed for all the attention lines La (S31: NO), the processing returns to step S23, a new attention line La is set, and the processing up to step S31 is repeated. On the other hand, when it is determined that the above process has been performed for all the attention lines La (S31: YES), the process proceeds to step S32 in FIG.

  In step S32 in FIG. 18, the three-dimensional object detection unit 37 calculates a luminance change along the edge line for each edge line detected in step S30 in FIG. 17. The three-dimensional object detection unit 37 calculates the luminance change of the edge line according to any one of the above formulas 4, 5, and 6. Next, in step S33, the three-dimensional object detection unit 37 excludes edge lines whose luminance change is larger than a predetermined threshold from the edge lines. That is, it is determined that an edge line having a large luminance change is not a correct edge line, and the edge line is not used for detecting a three-dimensional object. As described above, this is to prevent characters on the road surface, roadside weeds, and the like included in the detection area A1 from being detected as edge lines. Therefore, the predetermined threshold value is a value set based on a luminance change generated by characters on the road surface, weeds on the road shoulder, or the like obtained in advance by experiments or the like.

  Next, in step S34, the three-dimensional object detection unit 37 determines whether the amount of the edge line is equal to or greater than the second threshold value β. The second threshold value β can be obtained and set in advance by experiments or the like, and can be changed in accordance with a control command issued by the control unit 39 shown in FIG. 3, details of which will be described later. For example, when a four-wheeled vehicle is set as the three-dimensional object to be detected, the second threshold value β is set based on the number of edge lines of the four-wheeled vehicle that have appeared in the detection region A1 in advance through experiments or the like. When it is determined that the amount of the edge line is equal to or larger than the second threshold value β (S34: YES), the three-dimensional object detection unit 37 detects that a three-dimensional object exists in the detection area A1 in step S35. On the other hand, when it is determined that the amount of the edge line is not equal to or larger than the second threshold value β (S34: NO), the three-dimensional object detection unit 37 determines that there is no three-dimensional object in the detection area A1. Thereafter, the processing illustrated in FIGS. 17 and 18 ends. The detected three-dimensional object may be determined to be another vehicle VX that travels in the adjacent lane adjacent to the lane in which the host vehicle V travels, and the relative speed of the detected three-dimensional object with respect to the host vehicle V is taken into consideration. It may be determined whether the vehicle is another vehicle VX traveling in the adjacent lane. The second threshold value β can be set in advance, but can be changed according to a control command to the control unit 39.

  As described above, according to the three-dimensional object detection method using the edge information of the present embodiment, in order to detect the three-dimensional object existing in the detection areas A1 and A2, the vertical direction in the real space with respect to the bird's-eye view image A vertical imaginary line is set as a line segment extending to. Then, for each of a plurality of positions along the vertical imaginary line, a luminance difference between two pixels in the vicinity of each position can be calculated, and the presence or absence of a three-dimensional object can be determined based on the continuity of the luminance difference.

  Specifically, the attention line La corresponding to the line segment extending in the vertical direction in the real space and the reference line Lr different from the attention line La are set for the detection areas A1 and A2 in the bird's-eye view image. Then, a luminance difference between the attention point Pa on the attention line La and the reference point Pr on the reference line Lr is continuously obtained along the attention line La and the reference line La. In this way, the luminance difference between the attention line La and the reference line Lr is obtained by continuously obtaining the luminance difference between the points. In the case where the luminance difference between the attention line La and the reference line Lr is high, there is a high possibility that there is an edge of the three-dimensional object at the set position of the attention line La. Thereby, a three-dimensional object can be detected based on a continuous luminance difference. In particular, in order to compare brightness with vertical virtual lines extending in the vertical direction in real space, even if the three-dimensional object is stretched according to the height from the road surface by converting it to a bird's-eye view image, This detection process is not affected. Therefore, according to the method of this example, the detection accuracy of a three-dimensional object can be improved.

  In this example, the luminance difference between two points having substantially the same height near the vertical imaginary line is obtained. Specifically, the luminance difference is obtained from the attention point Pa on the attention line La and the reference point Pr on the reference line Lr, which are substantially the same height in the real space, and thus the luminance when there is an edge extending in the vertical direction. The difference can be detected clearly.

  Furthermore, in this example, the attention point Pa is attributed based on the luminance difference between the attention point Pa on the attention line La and the reference point Pr on the reference line Lr, and attribute continuity c along the attention line La is obtained. Therefore, it is determined whether the attention line La is an edge line. Therefore, the boundary between the high luminance area and the low luminance area is detected as an edge line, and edge detection is performed in accordance with a natural human sense. Can do. This effect will be described in detail. FIG. 19 is a diagram illustrating an example of an image for explaining the processing of the edge line detection unit 36. In this image example, a first striped pattern 101 showing a striped pattern in which a high brightness area and a low brightness area are repeated, and a second striped pattern showing a striped pattern in which a low brightness area and a high brightness area are repeated. 102 is an adjacent image. Further, in this image example, a region where the brightness of the first striped pattern 101 is high and a region where the brightness of the second striped pattern 102 is low are adjacent to each other, and a region where the brightness of the first striped pattern 101 is low and the second striped pattern 102. Is adjacent to a region with high brightness. The portion 103 located at the boundary between the first striped pattern 101 and the second striped pattern 102 tends not to be perceived as an edge depending on human senses.

  On the other hand, since the low luminance region and the high luminance region are adjacent to each other, if the edge is detected only by the luminance difference, the part 103 is recognized as an edge. However, since the edge line detection unit 36 determines the part 103 as an edge line only when there is continuity in the attribute of the luminance difference in addition to the luminance difference in the part 103, the edge line detection unit 36 An erroneous determination of recognizing a part 103 that is not recognized as an edge line as a sensation as an edge line can be suppressed, and edge detection according to a human sense can be performed.

  Furthermore, in this example, when the luminance change of the edge line detected by the edge line detection unit 36 is larger than a predetermined threshold value, it is determined that the edge line has been detected by erroneous determination. When the captured image acquired by the camera 10 is converted into a bird's-eye view image, the three-dimensional object included in the captured image tends to appear in the bird's-eye view image in a stretched state. For example, as described above, when the tire of the other vehicle VX is stretched, since one portion of the tire is stretched, the luminance change of the bird's-eye view image in the stretched direction tends to be small. On the other hand, when a character or the like drawn on the road surface is erroneously determined as an edge line, the bird's-eye view image includes a high luminance region such as a character portion and a low luminance region such as a road surface portion. In this case, the brightness change in the stretched direction tends to increase in the bird's-eye view image. Therefore, by determining the luminance change of the bird's-eye view image along the edge line as in this example, the edge line detected by the erroneous determination can be recognized, and the detection accuracy of the three-dimensional object can be improved.

《Final judgment of solid object》
Returning to FIG. 3, when detecting the three-dimensional object by the above-described two three-dimensional object detection unit 33 (or three-dimensional object detection unit 37), the three-dimensional object detection device 1 of this example includes a three-dimensional object determination unit 34, a similarity calculation unit, and the like. 38 and a control unit 39. Based on the detection result by the three-dimensional object detection unit 33 (or the three-dimensional object detection unit 37), the three-dimensional object determination unit 34 is the other vehicle VX that finally exists in the detection areas A1 and A2. Judge whether or not. It is determined whether or not the vehicle VX exists in the detection areas A1 and A2. The similarity calculation unit 38 calculates the similarity between the image information of the detection area A1 on the right rear side in the traveling direction of the host vehicle V and the image information on the detection area A2 on the rear left side in the traveling direction of the host vehicle V. When the similarity determined by the similarity calculation unit 38 is equal to or greater than a predetermined value, the control unit 39 determines that the detected three-dimensional object is the other vehicle V existing in the detection areas A1 and A2. A control command for controlling each unit (including the control unit 39) constituting the computer 30 is output so that the control is suppressed.

  The three-dimensional object determination unit 34 of the present embodiment determines whether or not the three-dimensional object detected by the three-dimensional object detection units 33 and 37 is the other vehicle VX existing in the detection areas A1 and A2. The three-dimensional object determination unit 34 can suppress the determination that the detected three-dimensional object is the other vehicle VX according to the control command of the control unit 38. Specifically, when the similarity calculated by the similarity calculation unit 38 is equal to or greater than a predetermined value, the control unit 38 suppresses determining that the detected three-dimensional object is the other vehicle VX. Specifically, the control unit 38 sends a control command for suppressing the detected three-dimensional object from being determined to be another vehicle VX to the three-dimensional object determination unit 34. The three-dimensional object determination unit 34 stops the determination process of the three-dimensional object according to this control command, or determines that the detected three-dimensional object is not the other vehicle VX, that is, there is no other vehicle VX in the detection areas A1 and A2. . Of course, when the control command is not acquired, it is possible to determine that the three-dimensional object detected by the three-dimensional object detection units 33 and 37 is the other vehicle VX existing in the detection areas A1 and A2.

  In the present embodiment, the similarity is calculated based on the image information of the detection area A1 on the right rear side (right side in the vehicle traveling direction) of the host vehicle V included in the image information obtained by the camera 10 and the rear of the host vehicle V. Similarity with the image information of the detection area A2 on the left side (right side in the vehicle traveling direction) is calculated. As shown in FIG. 20, the other vehicle VX has a symmetrical shape and appearance with respect to the central axis along the traveling direction. Therefore, when the other vehicle VX is behind the host vehicle V, that is, the host vehicle V is detected. The image information corresponding to the area A1 and the image information corresponding to the detection area A2 tend to include common features.

  The similarity calculation unit 38 of the present embodiment calculates the similarity between the image information of the detection area A1 and the detection area A2. The similarity in this embodiment is calculated based on the degree of correlation between the feature amounts of the image information in the detection area A1 and the detection area A2. In this embodiment, the degree of correlation is the degree of strength of association between variables of the feature amount of the image information, and is a relationship that increases and decreases together with regularity that exceeds a certain standard. The calculation method of the correlation degree is not particularly limited, and a calculation method of the correlation degree or the correlation coefficient known in the field of statistics at the time of filing can be appropriately applied. As an example, one obtained by dividing the covariance by the respective standard deviations can be used. The degree of correlation or the correlation coefficient is an index value indicating the correlation (degree of similarity) between two random variables. It can be expressed as a real value from -1 to 1 (no unit). When the difference from 1 is small, it can be evaluated that two random variables have a positive correlation with a relatively high degree of similarity. When the difference from -1 is small, it can be evaluated that there is a negative correlation with a relatively low degree of similarity. Incidentally, when it is close to 0, it can be said that the correlation of the original random variable is weak. Generally, if there is a correlation coefficient of 0.7 or more, it can be evaluated that the similarity is high.

  As the feature amount of the image information, difference waveform information, edge information, and brightness (luminance) of each pixel can be used. In particular, in the case of using differential waveform information and edge information, if the captured image is converted into a bird's-eye view image, the image of the region immediately behind the host vehicle V is stretched in the horizontal direction, so the image of the other vehicle VX directly behind the host vehicle V Enters the region corresponding to the adjacent lane, and therefore, there is a problem that the other vehicle VX directly behind the own vehicle V is erroneously detected as another vehicle traveling in the adjacent lane of the own vehicle V, as in the present embodiment. By eliminating the influence of the other vehicle VX directly behind, it is possible to prevent the other vehicle VX just behind the vehicle from being erroneously detected as another vehicle traveling in the adjacent lane of the host vehicle V.

  Although not particularly limited, the similarity calculation unit 38 is based on the degree of correlation between the feature amounts of the differential waveform information derived by the three-dimensional object detection unit 33 described above, and the similarity between the image information of the detection region A1 and the detection region A2. Can be calculated. As described above, the three-dimensional object detection unit 33 performs bird's-eye view of the positions of the bird's-eye view images at different times obtained by the viewpoint conversion unit 31 with respect to the detection region A1 on the right side behind the vehicle and the detection region A2 on the left side behind the vehicle. Difference waveform information is generated by performing the frequency alignment by counting the number of pixels in which the pixel value difference on the difference image of the aligned bird's-eye view image is equal to or greater than a predetermined threshold.

  Further, the similarity calculation unit 38 can calculate the similarity between the image information of the detection area A1 and the detection area A2 based on the feature of the edge information derived by the three-dimensional object detection unit 37 described above. As described above, the three-dimensional object detection unit 37 detects the right detection area behind the vehicle from the bird's-eye view image obtained by the viewpoint conversion unit 31 for the detection area A1 on the right side behind the vehicle and the detection area A2 on the left side behind the vehicle. Edge information of A1 and detection area A2 on the left side behind the vehicle is detected, and a three-dimensional object is detected based on these edge information. At this time, the three-dimensional object detection unit 37 generates edge information by detecting edges in the bird's-eye view image whose luminance difference between adjacent image regions is equal to or greater than a predetermined threshold.

  Note that the similarity calculation unit 38 does not use the differential waveform information generated by the three-dimensional object detection unit 33 or the edge information generated by the three-dimensional object detection unit 37, and each pixel information of the captured image obtained by the camera 10. The similarity between the image information of the detection area A1 and the detection area A2 can be calculated based on the degree of correlation between the two.

  The similarity calculation unit 38 can calculate the similarity between the image information of the detection area A1 and the detection area A2 based on the luminance feature of each pixel constituting the image information. When there is another vehicle VX directly behind the host vehicle V and the other vehicle VX is lit with a headlight, the image information has a pixel with high brightness at the center, and as the distance from the center increases. There are pixels whose luminance gradually decreases. That is, it is possible to detect a gradation pattern whose luminance gradually decreases from the center toward the end. In this way, the similarity between the image information of the detection area A1 and the detection area A2 can be calculated based on the luminance correlation of the pixel information of the captured image obtained by the camera 10.

  The similarity calculation unit 38 has a large rear distance from the camera 10 attached to the rear of the host vehicle V in the detection area A1 on the right side behind the host vehicle V and the detection area A2 on the left side rear of the host vehicle V. Weighting can be performed so that the region has a larger similarity value. When calculating the similarity, as shown in FIGS. 21A and 21B, the similarity calculation unit 38 defines the regions a1 to an according to the distance from the camera 10 (in the direction of arrow T in the figure), Weighting is performed so that the degree of similarity increases as the distance from the camera 10 increases, that is, as the position is farther from the camera 10. For example, the similarity calculation unit 38 sets the weighting factor K of the region a1 to be larger than the weighting factor K ′ of the region a10 so that the similarity of the region a1 is larger than the similarity of the region a10. Accordingly, the other vehicle VX running side by side with the host vehicle V is strictly detected, and the subsequent other vehicle VX traveling in the same lane as the host vehicle V is set to the adjacent lane adjacent to the travel lane of the host vehicle V. Can be prevented from being misidentified as the other vehicle VX traveling on the road. As a result, the detection accuracy of the other vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the host vehicle V can be improved.

  In addition, the similarity calculation unit 38 may perform weighting so that the similarity increases as the steering angle of the host vehicle V detected by the steering angle sensor 50 that detects the steering angle of the host vehicle V increases. it can. At this time, weighting can be performed so that the degree of similarity increases when the steering angle of the host vehicle V is equal to or greater than a predetermined value. As shown in FIGS. 22A and 22B, when the host vehicle is turning, the similarity between the left and right detection areas A1 and A2 of the other vehicle VX even if there is a subsequent other vehicle VX traveling in the same lane. Tend to be calculated low. This is the same whether the similarity is calculated based on the difference waveform information or the similarity is calculated based on the edge information. For this reason, the similarity calculation unit 38 corrects the similarity to a high value. Although not particularly limited, a larger weighting coefficient can be set as the turning radius is smaller, that is, as the steering angle is increased. Thereby, the fall of the detection accuracy of the following vehicle of the same lane which arises when the own vehicle V turns can be prevented. As a result, the detection accuracy of the other vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the host vehicle V can be improved.

  Furthermore, the similarity calculation unit 38 performs weighting so that the similarity is increased when the luminances of the right detection area A1 and the left detection area A2 behind the host vehicle V are equal to or higher than a predetermined value. Can do. When there is another vehicle VX behind the host vehicle V and the other vehicle VX lights the headlight, if the headlight light extends and is inserted into the detection areas A1 and A2, the influence of this light There is a tendency to misidentify the following other vehicle VX as the other vehicle VX traveling in the adjacent lane. For this reason, in this embodiment, when the brightness | luminance of detection area A1 and detection area A2 is more than predetermined value, it weights so that a similarity degree may become large.

  The similarity calculation unit 38 outputs the calculated similarity to the three-dimensional object determination unit 34. At that time, the calculated similarity value is not output as it is, but can be output by increasing or decreasing according to the control command generated and output by the control unit 39 described above.

  Next, the control unit 39 will be described. When the similarity calculated by the similarity calculation unit 38 in the previous process is greater than or equal to a predetermined value, the control unit 39 according to the present embodiment performs the three-dimensional object detection units 33 and 37 and the three-dimensional object determination unit in the next process. 34, it is possible to generate a control command to be executed in any one or more of the similarity calculation unit 38 or the control unit 39 that is itself. Although not particularly limited, whether or not the degree of similarity is equal to or greater than a predetermined value depends on whether the absolute value of the correlation coefficient of the feature amount of the difference waveform information or edge information in the two detection areas A1 and A2 is, for example, a predetermined value 0. It can be a case of 7 or more. The predetermined value can be appropriately set according to the detection environment of day and night, weather, and other three-dimensional objects.

  The control command of the present embodiment is a command for controlling the operation of each unit so that it is suppressed that the detected three-dimensional object is the other vehicle VX. This is to prevent the other vehicle VX immediately behind the host vehicle VX from being erroneously determined as the other vehicle VX traveling in the adjacent lane that is the detection target. Since the computer 30 of the present embodiment is a computer, control commands for the three-dimensional object detection process, the three-dimensional object determination process, and the similarity calculation process may be incorporated in advance in the program of each process, or may be transmitted at the time of execution. The control command of the present embodiment may be a command for a result of stopping the process of determining the detected three-dimensional object as another vehicle, or determining that the detected three-dimensional object is not another vehicle, A command for reducing the sensitivity when detecting a three-dimensional object based on the difference waveform information, a command for adjusting the sensitivity when detecting a three-dimensional object based on edge information, and the similarity between the image information of the two detection areas A1 and A2 It may be a command for a processing process such as a command for adjusting a threshold when evaluating the image, a command for adjusting an area for acquiring image information, and the like.

Hereinafter, each control command output by the control unit 39 will be described.
First, a control command for detecting a three-dimensional object based on differential waveform information will be described. As described above, the three-dimensional object detection unit 33 detects a three-dimensional object based on the difference waveform information and the first threshold value α. Then, the control unit 39 of the present embodiment outputs a control command for increasing the first threshold value α to the three-dimensional object detection unit 33 when the similarity calculated by the similarity calculation unit 38 is equal to or greater than a predetermined value. . The first threshold value α is the first threshold value α for determining the peak of the differential waveform DW _t in step S7 of FIG. 11 (see FIG. 5). In addition, the control unit 39 can output a control command for increasing the threshold value p regarding the difference between pixel values in the difference waveform information to the three-dimensional object detection unit 33.

  When the similarity greater than or equal to the predetermined value is calculated in the previous process, the control unit 39 can determine that there is a high possibility that the other vehicle VX exists immediately behind the host vehicle V. If there is another vehicle VX directly behind the host vehicle V, it can be determined that there is a high possibility that the image of the other vehicle VX directly behind is reflected in the detection areas A1 and A2. If a three-dimensional object is detected in the same manner as usual, the other vehicle VX that travels in the detection areas A1 and A2 based on the image of the other vehicle VX directly behind, although the other vehicle VX does not exist in the detection areas A1 and A2. May be erroneously detected. For this reason, the first threshold value α or the threshold value p regarding the difference between the pixel values when generating the difference waveform information is changed to be high so that the three-dimensional object is not easily detected in the next processing. In this way, by changing the determination threshold value higher, the detection sensitivity is adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is difficult to be detected. It is possible to prevent erroneous detection as the other vehicle VX traveling in the lane.

In addition, when the similarity calculated by the similarity calculation unit 38 is equal to or greater than a predetermined value, the control unit 39 according to the present embodiment counts the number of pixels indicating a predetermined difference on the difference image of the bird's eye view image. Thus, a control command for outputting the frequency-distributed value low can be output to the three-dimensional object detection unit 33. The value obtained by counting the number of pixels indicating a predetermined difference on the difference image of the bird's-eye view image and performing frequency distribution is the value on the vertical axis of the difference waveform DW _t generated in step S5 of FIG. When the similarity greater than or equal to the predetermined value is calculated in the previous process, the control unit 39 can determine that there is a high possibility that another vehicle VX exists immediately behind the host vehicle V. The frequency-distributed value of the differential waveform DW _t is changed to be low so that it is difficult to detect. In this way, by reducing the output value, the detection sensitivity is adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is not easily detected, so that the following other vehicle VX is moved to the next lane. It is possible to prevent erroneous detection as the traveling other vehicle VX.

  Next, a control command for detecting a three-dimensional object based on edge information will be described. When the similarity calculated by the similarity calculation unit 38 is equal to or greater than a predetermined value, the control unit 39 according to the present embodiment outputs a control command for increasing a predetermined threshold related to luminance used when detecting edge information. It outputs to the object detection part 37. The predetermined threshold value relating to the luminance used when detecting edge information is the threshold value θ for determining a value obtained by normalizing the sum of the continuity c of the attributes of each point of interest Pa in step S29 in FIG. 17, or the step in FIG. 34 is a second threshold value β for evaluating the amount of edge lines. The control unit 39 can determine that there is a high possibility that the other vehicle VX exists immediately behind the host vehicle V when the similarity equal to or greater than the predetermined value is calculated in the previous process. The threshold value θ used for detecting the edge line or the second threshold value β for evaluating the amount of the edge line is changed to be high so that an object is difficult to detect. In this way, by changing the determination threshold value higher, the detection sensitivity is adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is difficult to be detected. It is possible to prevent erroneous detection as the other vehicle VX traveling in the lane.

  In addition, when the similarity calculated by the similarity calculation unit 38 is equal to or greater than a predetermined value, the control unit 39 of the present embodiment issues a control command for outputting a low amount of detected edge information to the three-dimensional object detection unit 37. Output to. The detected amount of edge information is a value obtained by normalizing the sum of the continuity c of the attributes of each point of interest Pa in step S29 in FIG. 17 or the amount of edge lines in step 34 in FIG. When the similarity greater than or equal to the predetermined value is calculated in the previous process, the control unit 39 can determine that there is a high possibility that another vehicle VX exists immediately behind the host vehicle V. In order to make it difficult to detect, the value obtained by normalizing the sum of the continuity c of the attribute of each attention point Pa or the amount of the edge line is changed to be low. Thus, by lowering the output value, the detection sensitivity can be adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is difficult to be detected, so that the following other vehicle VX travels in the adjacent lane. It is possible to prevent erroneous detection as the other vehicle VX.

  Furthermore, the control part 39 of this embodiment changes the predetermined value which judges a similarity low as the steering angle of the vehicle detected by the steering angle sensor 50 becomes large. At this time, when the steering angle is equal to or greater than a predetermined value, the predetermined value for determining the similarity may be changed to a low value. As a result, while turning, it is difficult to detect the following other vehicle VX. However, in order to ease the similarity determination, the above-described processing for suppressing erroneous detection is easily performed, and thus the following other vehicle VX. Can be prevented from being erroneously detected as the other vehicle VX traveling in the adjacent lane.

  Furthermore, when the brightness of the right detection area A1 and the left detection area A2 behind the host vehicle V is equal to or higher than a predetermined value, the control unit 39 of the present embodiment decreases the predetermined value for determining the similarity. To do. The brightness of the detection areas A1 and A2 can be detected from the captured image of the camera 10. When the brightness of the detection areas A1 and A2 is higher than a predetermined threshold, there is a possibility that the light of the headlight of the other vehicle VX behind is inserted into the detection areas A1 and A2. Although the headlight light image may be erroneously detected as another vehicle VX traveling in an adjacent lane, the above-described processing for suppressing erroneous detection can be easily performed by lowering the predetermined value for determining the similarity. Therefore, it is possible to prevent the subsequent other vehicle VX from being erroneously detected as the other vehicle VX traveling in the adjacent lane. Note that the threshold value (predetermined value) for determining the brightness level varies depending on the vehicle height, the installation position of the camera 10, and the like, and can be set experimentally.

  Further, the control command for adjusting each threshold value or each output value can include an adjustment coefficient corresponding to the similarity calculated by the similarity calculation unit 38. Thereby, each threshold value or each output value can be adjusted according to the similarity. In this case, the adjustment coefficient is a coefficient that is adjusted so that the threshold value is higher (stricter) as the similarity is higher, and the output value is lower (a value that is difficult to be determined as a three-dimensional object) as the similarity is higher. The coefficient is adjusted as follows. Each adjusted threshold value or each output value may be changed linearly according to the change in the degree of similarity, or may be changed stepwise.

  From a different point of view, the control command for adjusting each threshold value or each output value can include an adjustment coefficient according to the positions of the detection areas A1 and A2 with respect to the camera 10 (rear distance from the camera 10). Thereby, each threshold value or each output value can be adjusted according to the rear distance from the camera 10. In this case, the adjustment coefficient is a coefficient that is adjusted so that the threshold value decreases as the distance from the camera 10 increases (a value that is difficult to determine as a three-dimensional object), and the threshold increases as the distance from the camera 10 decreases. The coefficient is adjusted to be (strict). Each adjusted threshold value or each output value may be changed continuously according to the change in the degree of similarity, or may be changed discontinuously (stepwise).

  In addition, the control unit 39 of the present embodiment, when the similarity is equal to or greater than a predetermined value in the previous process, out of the image information obtained by the camera 10, the rear distance from the camera 10 is within the predetermined distance. A control command for detecting a three-dimensional object based on the image information of the partial area is output to the three-dimensional object detection units 33 and 37. As shown in FIG. 23, the control unit 39 masks the partial areas A1 ″ and A2 ″ whose distance along the rear side (arrow T) from the camera 10 is longer than the predetermined distance T1, and within a predetermined distance T1. A control command for detecting a three-dimensional object is generated based on the image information of the partial areas A1 ′ and A2 ′, and this is output to the three-dimensional object detection units 33 and 37. As a result, when there is a high possibility that the following other vehicle VX exists, the three-dimensional object is not detected in the region far from the host vehicle V and only the partial regions A1 ′ and A2 ′ close to the host vehicle V are detected. A three-dimensional object can be detected. As a result, it is possible to reliably detect the other vehicle VX running in parallel with the host vehicle V while preventing the subsequent other vehicle VX from being erroneously detected as the other vehicle VX traveling in the adjacent lane.

  Hereinafter, the operations of the control unit 39 and the three-dimensional object determination unit 34 and the three-dimensional object detection units 33 and 37 that have acquired the control command will be described with reference to FIGS. The process illustrated in FIGS. 24 to 28 is the current three-dimensional detection process performed using the result of the previous process after the previous three-dimensional object detection process.

  First, in step S <b> 41 shown in FIG. 24, the similarity calculation unit 38 detects the difference waveform information of the left and right detection areas A <b> 1 and A <b> 2 generated by the three-dimensional object detection unit 33 or the left and right detections generated by the three-dimensional object detection unit 37. The similarity is calculated based on the edge information of the areas A1 and A2. When calculating the similarity, the similarity calculation unit 38 can perform weighting so that the region having a larger rear distance from the camera 10 has a higher similarity value. Further, when calculating the similarity, the similarity calculation unit 38 can weight the detection areas A1 and A2 so that the similarity is increased when the brightness of the detection areas A1 and A2 is equal to or higher than a predetermined value. Furthermore, weighting can be performed so that the degree of similarity increases as the steering angle of the host vehicle V detected by the steering angle sensor 50 increases. At this time, weighting may be performed so that the degree of similarity increases when the steering angle of the host vehicle V is equal to or greater than a predetermined value. Any one of these weights may be performed, or a plurality of weights may be performed simultaneously.

  Next, in step 42, the control unit 39 determines whether or not the similarity calculated in step 41 is a predetermined value or more. Here, when the host vehicle V is turning and the steering angle sensor 50 detects a steering angle greater than or equal to a predetermined value, the control unit 39 changes the predetermined value, which is a threshold for determining similarity, to a low value. The result that the similarity is not less than a predetermined value is easily derived. As a result, it is possible to correct the inconvenience that the degree of similarity is easily calculated during turning. Further, when the luminance of the detection areas A1 and A2 is equal to or higher than the predetermined value, the control unit 39 changes the predetermined value, which is a threshold value for determining similarity, to be low, and the similarity is determined to be higher than the predetermined value. To be guided easily. Accordingly, it is possible to carefully detect the three-dimensional object so that the subsequent other vehicle VX and the other vehicle VX traveling in the adjacent lane are not misidentified due to the influence of the headlight of the subsequent other vehicle VX at night.

  The control unit 39 outputs a control command to each unit so that it is suppressed that the detected three-dimensional object is the other vehicle VX when the similarity is equal to or greater than a predetermined value. As an example, the process proceeds to step S46, and the control unit 39 outputs a control command with a content to stop the detection processing of the three-dimensional object to the three-dimensional object determination unit 34. As another example, the process proceeds to step S47, and the control unit 39 can determine that the detected three-dimensional object is not the other vehicle VX.

  If the similarity is less than the predetermined value, the process proceeds to step S43 to perform a three-dimensional object detection process. This three-dimensional object detection processing is performed according to the above-described processing using the difference waveform information of FIGS. 11 and 12 by the three-dimensional object detection unit 33 or the processing using the edge information of FIGS. 17 and 18 by the three-dimensional object detection unit 37. Is called. In step 43, if a three-dimensional object is detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S45, and it is determined that the detected three-dimensional object is the other vehicle VX. On the other hand, when a solid object is not detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S47, and it is determined that no other vehicle VX exists in the detection areas A1 and A2.

FIG. 25 shows another processing example. If it is determined in step 42 that the degree of similarity is greater than or equal to the predetermined value, the control unit 39 proceeds to step S51 and determines the three-dimensional from the threshold value p and the difference waveform information regarding the difference between the pixel values when generating the difference waveform information. Control for setting one or more of the first threshold value α used when judging an object, the threshold value θ when generating edge information, and the second threshold value β used when judging a solid object from edge information to be high The command is sent to the three-dimensional object detection units 33 and 37. As described above, the first threshold value α is used to determine the peak of the differential waveform DW _t in step S7 of FIG. The threshold value θ is a threshold value for determining a value obtained by normalizing the sum of the continuity c of the attribute of each target point Pa in step S29 in FIG. 17, and the second threshold value β is the amount of the edge line in step 34 in FIG. Is a threshold value for evaluating. Note that these threshold values may be changed to predetermined values when it is determined that the degree of similarity is equal to or greater than a predetermined value, but the threshold values may be changed according to the rear distance from the camera 10. FIG. 26 shows an example of threshold values (first threshold value α, threshold value p second threshold value β, threshold value θ) corresponding to the rear distance. As described above, by increasing the threshold as the distance from the camera 10 increases, it is possible to make it difficult to detect a three-dimensional object in a far region behind. As a result, when the degree of similarity is equal to or higher than the predetermined value and the other vehicle VX is present, the other vehicle VX is prevented from being misidentified as the other vehicle VX traveling in the adjacent lane, while the other region close to the camera 10 is not. The vehicle can be detected accurately.

As shown in FIG. 27, when it is determined in step 42 that the similarity is greater than or equal to a predetermined value, the control unit 39 proceeds to step S52 and displays a predetermined difference on the difference image of the bird's eye view image. A control command for counting the number of pixels shown and outputting a frequency distribution value low is output to the three-dimensional object detection unit 33. The value obtained by counting the number of pixels indicating a predetermined difference on the difference image of the bird's-eye view image and performing frequency distribution is the value on the vertical axis of the difference waveform DW _t generated in step S5 of FIG. Similarly, in step S52, a control command for outputting the detected amount of edge information low is output to the three-dimensional object detection unit 37. The detected amount of edge information is a value obtained by normalizing the sum of the continuity c of the attributes of each point of interest Pa in step S29 in FIG. 17 or the amount of edge lines in step 34 in FIG. When the similarity greater than or equal to the predetermined value is calculated in the previous process, the control unit 39 can determine that there is a high possibility that another vehicle VX exists immediately behind the host vehicle V. A control command for changing the normalized sum of the continuity c of the attribute of each attention point Pa or changing the amount of the edge line so as to be difficult to detect is output to the three-dimensional object detection unit 37.

  FIG. 28 shows still another processing example. If it is determined in step 42 that the degree of similarity is greater than or equal to a predetermined value, the control unit 39 proceeds to step S53, and among the detection areas A1 and A2, a rear area (see FIG. 23 detection areas A1 ″ and A2 ″) are masked, and the three-dimensional object detection processing is performed only in front areas (detection areas A1 ′ and A2 ′ in FIG. 23) that are less than a predetermined distance from the camera 10. A control command is sent to the three-dimensional object detection unit 33. The three-dimensional object detection unit 33 performs a three-dimensional object detection process in accordance with this control command.

  (1) As described above, according to the three-dimensional object detection device 1 of the present embodiment, the similarity between the image information of the right detection area A1 behind the host vehicle V and the image information of the left detection area A2 is a predetermined value or more. In some cases, it is determined that there is a high possibility that another vehicle VX exists immediately behind the host vehicle V, and control is performed so that the three-dimensional object is not determined to be the other vehicle VX traveling in the adjacent lane. It is possible to prevent erroneous detection of the other vehicle VX traveling in the adjacent lane based on the image of the other vehicle VX existing immediately behind the vehicle. As a result, the other vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the host vehicle V can be detected with high accuracy.

  (2) According to the three-dimensional object detection device 1 of the present embodiment, when the similarity is greater than or equal to the predetermined value, the three-dimensional object detection process is stopped, so that there is another vehicle V behind the host vehicle V. When predicted, it is possible to prevent the subsequent other vehicle VX from being erroneously detected as the other vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the host vehicle V.

  (3) Since the differential waveform information is generated from the bird's eye view image and the similarity of the differential waveform information is determined, it can be accurately determined whether or not the other vehicle V exists behind the host vehicle V. .

  (4) If the degree of similarity is higher than the predetermined value in the previous process, the first threshold value α is changed to be higher so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is not easily detected. Since the detection sensitivity can be adjusted, it is possible to prevent the subsequent other vehicle VX from being erroneously detected as the other vehicle VX traveling in the adjacent lane.

  (5) If the degree of similarity is higher than a predetermined value in the previous process, the other vehicle VX traveling next to the traveling lane of the host vehicle V is reduced by lowering the output value when generating the differential waveform information. Since the detection sensitivity can be adjusted so that it is difficult to be detected, it is possible to prevent the subsequent other vehicle VX from being erroneously detected as the other vehicle VX traveling in the adjacent lane.

  (6) Since edge information is generated from the bird's-eye view image and the similarity of the edge information is determined, it is possible to accurately determine whether or not another vehicle V exists behind the host vehicle V.

  (7) If the degree of similarity is higher than a predetermined value in the previous process, the other vehicle VX that travels next to the traveling lane of the host vehicle V is changed by changing the threshold value for determination when generating edge information to be higher. Since the detection sensitivity can be adjusted so that it is difficult to be detected, it is possible to prevent erroneous detection of the subsequent other vehicle VX as the other vehicle VX traveling in the adjacent lane.

  (8) When the similarity is higher than a predetermined value in the previous process, the other vehicle VX traveling next to the traveling lane of the host vehicle V is detected by lowering the output value when generating edge information. Since the detection sensitivity can be adjusted so that it is difficult to be detected, it is possible to prevent the subsequent other vehicle VX from being erroneously detected as the other vehicle VX traveling in the adjacent lane.

  (9) When calculating the similarity, weighting is performed so that the value of the similarity increases in the region where the rear distance from the camera 10 attached to the rear of the host vehicle V is larger. While the other vehicle VX running side by side is strictly detected, the following other vehicle VX traveling in the same lane as the own vehicle V is set as the other vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the own vehicle V. Misidentification can be prevented. As a result, the detection accuracy of the other vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the host vehicle V can be improved.

  (10) When calculating the similarity, if the luminance of the right detection area A1 and the left detection area A2 behind the host vehicle V is equal to or higher than a predetermined value, weighting is performed so as to increase the similarity. Do. As a result, when another vehicle VX exists behind the host vehicle V and the other vehicle VX lights up the headlight, the light from the headlight extends and is inserted into the detection areas A1 and A2. It is possible to prevent the subsequent other vehicle VX from being misidentified as the other vehicle VX traveling in the adjacent lane due to the influence of the above.

  (11) When calculating the similarity, weighting is performed so that the similarity increases as the steering angle of the host vehicle V increases, so that the subsequent of the same lane generated by the host vehicle V turning. A decrease in vehicle detection accuracy can be prevented. As a result, the detection accuracy of the other vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the host vehicle V can be improved.

  (12) As the steering angle increases, the predetermined value set for determining the similarity is increased to make it difficult to detect the following other vehicle VX during the turn, but the similarity is determined. Since the processing for suppressing the erroneous detection described above is easily performed in order to make sweet, it is possible to prevent the subsequent other vehicle VX from being erroneously detected as the other vehicle VX traveling in the adjacent lane. Can be corrected.

  (13) When the brightness of the detection areas A1 and A2 is higher than a predetermined threshold value, the predetermined value for judging the similarity is lowered, so that the image by the headlight is used as the other vehicle VX traveling in the adjacent lane. It is possible to prevent erroneous detection.

  (14) When the similarity is equal to or greater than a predetermined value, among the image information obtained by the camera 10, a three-dimensional object is detected based on image information of a partial area whose rear distance from the camera 10 is within the predetermined distance. Therefore, the three-dimensional object can be detected only in the partial areas A1 ′ and A2 ′ close to the host vehicle V. As a result, it is possible to reliably detect the other vehicle VX running in parallel with the host vehicle V while preventing the subsequent other vehicle VX from being erroneously detected as the other vehicle VX traveling in the adjacent lane.

  (15) In the present embodiment, the same action and the same effect can also be obtained in the method for detecting a three-dimensional object.

  The camera 10 corresponds to an imaging unit according to the present invention, the viewpoint conversion unit 31 corresponds to an image conversion unit according to the present invention, and the alignment unit 32 and the three-dimensional object detection unit 33 include a three-dimensional object detection according to the present invention. The brightness difference calculation unit 35, the edge line detection unit 36, and the three-dimensional object detection unit 37 correspond to a three-dimensional object detection unit according to the present invention, and the three-dimensional object determination unit 34 corresponds to a three-dimensional object determination unit. The similarity calculation unit 38 corresponds to similarity calculation means, the control unit 39 corresponds to control means, the steering angle sensor 50 corresponds to steering angle detection means, and the vehicle speed sensor 20 corresponds to vehicle speed sensor. To do.

DESCRIPTION OF SYMBOLS 1 ... Three-dimensional object detection apparatus 10 ... Camera 20 ... Vehicle speed sensor 30 ... Computer 31 ... Viewpoint conversion part 32 ... Position alignment part 33, 37 ... Three-dimensional object detection part 34 ... Three-dimensional object judgment part 35 ... Luminance difference calculation part 36 ... Edge detection Unit 38 ... similarity calculation unit 39 ... control unit 40 ... smear detection unit 50 ... steering angle sensor a ... angle of view A1, A2 ... detection region CP ... intersection point DP ... difference pixel DW _t , DW _t '... difference waveform DW _t1 ~ DW _m , DW _{m + k to} DW _tn ... small areas L1, L2 ... ground lines La, Lb ... lines P in the direction in which the three-dimensional object collapses ... captured image PB _t ... bird's-eye view image PD _t ... difference image MP ... mask image S ... Smear SP ... Smear image SB _t ... Smear bird's-eye view image V ... Own vehicle VX ... Other vehicle

Claims (15)

One imaging means mounted on the vehicle and imaging the rear of the vehicle;
Three-dimensional object detection means for detecting a three-dimensional object present in the right detection area or the left detection area behind the vehicle based on image information obtained by the imaging means;
Three-dimensional object determination means for determining whether the three-dimensional object detected by the three-dimensional object detection means is another vehicle existing in the right detection area or the left detection area;
Similarity calculation means for calculating the similarity between the image information of the right detection area behind the vehicle and the image information of the left detection area behind the vehicle;
Control means for generating a control command for suppressing that the detected three-dimensional object is the other vehicle when the similarity calculated by the similarity calculation means is a predetermined value or more; A three-dimensional object detection apparatus.   When the similarity is equal to or higher than a predetermined value, the control means has a control instruction for canceling the three-dimensional object determination process or a control instruction for determining that the detected three-dimensional object is not another vehicle. The three-dimensional object detection apparatus according to claim 1, wherein the three-dimensional object determination unit generates and outputs to the three-dimensional object determination unit. Further comprising image conversion means for converting the image of the right detection area or the left detection area behind the vehicle obtained by the imaging means into a bird's eye view image,
The three-dimensional object detection means aligns the positions of the bird's-eye view images at different times obtained by the image conversion means on the bird's-eye view, and the difference between the pixel values on the difference image of the aligned bird's-eye view images. Differential waveform information of the right detection area behind the vehicle and the left detection area behind the vehicle is generated by counting the number of pixels that are equal to or greater than a predetermined threshold value, and the vehicle is generated based on the difference waveform information. The three-dimensional object detection device according to claim 1, wherein a three-dimensional object existing in a rear right detection area or a left detection area is detected. The similarity calculation means calculates the similarity between the two differential waveform information of the generated right detection area and left detection area behind the vehicle,
The three-dimensional object detection means detects a three-dimensional object based on the difference waveform information and the first threshold value α,
The three-dimensional object according to claim 3, wherein the control means outputs a control command for increasing the first threshold value α to the three-dimensional object detection means when the similarity is equal to or greater than a predetermined value. Detection device. The similarity calculation means calculates the similarity between the two differential waveform information of the generated right detection area and left detection area behind the vehicle,
When the similarity is equal to or higher than a predetermined value, the control means counts the number of pixels indicating a predetermined difference on the difference image of the bird's eye view image and lowers the frequency distribution value. The three-dimensional object detection apparatus according to claim 3, wherein the three-dimensional object detection device generates and outputs the control command to the three-dimensional object detection unit. Further comprising image conversion means for converting the image obtained by the imaging means into a bird's-eye view image,
The three-dimensional object detection unit detects edge information in which a luminance difference between adjacent image regions is equal to or greater than a predetermined threshold among the bird's-eye view images obtained by the image conversion unit, and based on the edge information, The three-dimensional object detection apparatus according to claim 1, wherein a three-dimensional object existing in the right detection area or the left detection area is detected. The similarity calculating means calculates the similarity between the generated edge information of the right side detection area behind the vehicle and the left side detection area behind the vehicle,
The three-dimensional object detection means detects a three-dimensional object based on the edge information and the second threshold value β,
The three-dimensional object according to claim 6, wherein the control unit outputs a control command for increasing the second threshold value β to the three-dimensional object detection unit when the similarity is equal to or greater than a predetermined value. Detection device. The similarity calculating means calculates the similarity between the generated edge information of the right side detection area behind the vehicle and the left side detection area behind the vehicle,
The said control means outputs the control command which outputs the amount of the detected edge information low to the said solid-object detection means, when the said similarity is more than predetermined value. Three-dimensional object detection device.   The similarity calculation means is configured such that, in the right detection area at the rear of the vehicle and the left detection area at the rear of the vehicle, the value of the similarity is larger in a region where the rear distance from the imaging means attached to the rear of the vehicle is larger. The three-dimensional object detection apparatus according to any one of claims 1 to 8, wherein weighting is performed so as to increase.   The similarity calculation means weights the similarity so as to increase when the luminance of the right detection area behind the vehicle and the left detection area behind the vehicle is equal to or greater than a predetermined value. The three-dimensional object detection device according to any one of claims 1 to 9. A steering angle detecting means for detecting a steering angle of the vehicle;
The said similarity calculation means weights so that the said similarity may become large as the steering angle of the vehicle detected by the said steering angle detection means becomes large. The three-dimensional object detection device according to one item. A steering angle detecting means for detecting a steering angle of the vehicle;
The control means generates a control command for lowering the predetermined value for judging the similarity as the vehicle steering angle detected by the steering angle detection means increases. The three-dimensional object detection device according to any one of the above.   The said control means produces | generates the control command which makes the said predetermined value low which judges the said similarity, when the brightness | luminance of the right side detection area of the said vehicle rear and the left side detection area of the said vehicle rear is more than predetermined value. Item 13. The three-dimensional object detection device according to any one of Items 1 to 12.   When the similarity is greater than or equal to a predetermined value, the control means is based on image information of a partial area whose rear distance from the imaging means is within a predetermined distance among the image information obtained by the imaging means. The three-dimensional object detection apparatus according to claim 1, wherein a control command for detecting a three-dimensional object is generated, and the control command is output to the three-dimensional object detection unit. Obtain image information behind the vehicle imaged by a camera mounted on the vehicle,
Detecting a three-dimensional object present in the right detection area or the left detection area behind the vehicle based on the image information,
Determining whether the detected three-dimensional object is another vehicle existing in the right detection area or the left detection area;
Calculating the similarity between the image information of the right detection area behind the vehicle and the image information of the left detection area behind the vehicle;
A solid object detection method for generating a control command for suppressing a determination that the solid object is the other vehicle when the calculated similarity is equal to or greater than a predetermined value.

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