JP5790867B2 - Three-dimensional object detection device - Google Patents

Description

The present invention relates to a three-dimensional object detection device.
This application claims priority based on Japanese Patent Application No. 2012-046659 filed on Mar. 2, 2012. For designated countries that are allowed to be incorporated by reference, 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.

  2. Description of the Related Art Conventionally, a technique is known in which two captured images captured at different times are converted into a bird's-eye view image, and an obstacle is detected based on the difference between the two converted bird's-eye view images (Patent Literature). 1).

JP 2008-227646 A

  In the case of detecting other vehicles existing in the detection region using an image obtained by imaging the rear of the own vehicle, when the own vehicle overtakes the other vehicle, an image of the light of the headlight of the other vehicle that the own vehicle has overtaken, In some cases, an image in which another vehicle that the host vehicle overtakes is reflected in a puddle is erroneously detected as another vehicle traveling in the adjacent lane.

  The problem to be solved by the present invention is to provide a three-dimensional object detection device capable of appropriately detecting another vehicle as a detection target.

  This invention solves the said subject by detecting a solid object in the detection object area | region except the mask area | region behind the advancing direction among detection areas, when the own vehicle overtakes another vehicle.

  According to the present invention, when the host vehicle overtakes another vehicle, the three-dimensional object is detected only in the detection target region excluding the mask region behind the traveling direction in the detection region. The influence of the light of the headlight of the other vehicle irradiated to the mask area can be eliminated, and accordingly, the other vehicle traveling in the adjacent lane can be detected appropriately.

FIG. 1 is a schematic configuration diagram of a vehicle equipped with the three-dimensional object detection device according to the first embodiment. FIG. 2 is a plan view showing a traveling state of the vehicle of FIG. FIG. 3 is a block diagram showing details of the computer according to the first embodiment. FIGS. 4A and 4B are diagrams for explaining the outline of the processing of the alignment unit according to the first embodiment, in which FIG. 4A is a plan view showing the moving state of the vehicle, and FIG. 4B is an image showing the outline of alignment. It is. FIG. 5 is a schematic diagram illustrating how a differential waveform is generated by the three-dimensional object detection unit according to the first embodiment. FIG. 6 is a diagram illustrating a small region divided by the three-dimensional object detection unit according to the first embodiment. FIG. 7 is a diagram illustrating an example of a histogram obtained by the three-dimensional object detection unit according to the first embodiment. FIG. 8 is a diagram illustrating weighting by the three-dimensional object detection unit according to the first embodiment. FIG. 9 is a diagram illustrating another example of a histogram obtained by the three-dimensional object detection unit according to the first embodiment. FIG. 10 is a diagram for explaining a method of determining other vehicles existing in the adjacent lane. FIG. 11 is a graph showing the relationship between the luminance and the threshold value α. FIG. 12 is a diagram for explaining a method for detecting the degree of darkness of an image. FIG. 13 is a diagram for explaining a method of setting a detection area during overtaking. FIG. 14 is a flowchart illustrating the adjacent vehicle detection method according to the first embodiment. FIG. 15 is a flowchart illustrating the detection control processing method according to the first embodiment. FIG. 16 is a block diagram illustrating details of the computer according to the second embodiment. FIGS. 17A and 17B are diagrams illustrating a traveling state of the vehicle, in which FIG. 17A is a plan view illustrating the positional relationship of the detection region and the like, and FIG. 17B is a perspective view illustrating the positional relationship of the detection region and the like in real space. FIG. 18 is a diagram for explaining the operation of the luminance difference calculation unit according to the second embodiment. FIG. 18A is a diagram showing the positional relationship among the attention line, reference line, attention point, and reference point in the bird's-eye view image. (B) is a figure which shows the positional relationship of the attention line, reference line, attention point, and reference point in real space. 19A and 19B are diagrams for explaining the detailed operation of the luminance difference calculation unit according to the second embodiment. FIG. 19A is a diagram showing a detection area in a bird's-eye view image, and FIG. 19B is a note in the bird's-eye view image. It is a figure which shows the positional relationship of a line, a reference line, an attention point, and a reference point. FIG. 20 is a diagram illustrating an example of an image for explaining the edge detection operation. 21A and 21B are diagrams showing edge lines and luminance distribution on the edge lines. FIG. 21A is a diagram showing the luminance distribution when a three-dimensional object (adjacent vehicle) is present in the detection area, and FIG. 21B is a detection area. It is a figure which shows luminance distribution when a solid object does not exist in FIG. FIG. 22 is a flowchart illustrating an adjacent vehicle detection method according to the second embodiment.

<< First Embodiment >>
FIG. 1 is a schematic configuration diagram of a vehicle equipped with a three-dimensional object detection device 1 according to the first embodiment. The three-dimensional object detection device 1 according to the present embodiment is intended to detect other vehicles (hereinafter also referred to as adjacent vehicles) existing in adjacent lanes that may be contacted when the host vehicle V1 changes lanes. To do. As illustrated in FIG. 1, the three-dimensional object detection device 1 according to the present embodiment includes a camera 10, a vehicle speed sensor 20, a calculator 30, and a communication device 40.

  As shown in FIG. 1, the camera 10 is attached to the vehicle V <b> 1 so that the optical axis is at an angle θ from the horizontal to the lower side at the position of the height h behind the host vehicle V <b> 1. The camera 10 captures an image of a predetermined area in the surrounding environment of the host vehicle V1 from this position. The vehicle speed sensor 20 detects the traveling speed of the host vehicle V1, 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 an adjacent vehicle existing in an adjacent lane behind the host vehicle. The communication device 40 communicates with an external server (not shown) installed outside the host vehicle, and acquires current weather information (information such as clear weather and rainy weather) around the host vehicle from the external server. The weather information acquired by the communication device 40 is transmitted to the computer 30.

  FIG. 2 is a plan view showing a traveling state of the host vehicle V1 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 (adjacent lanes) can be imaged in addition to the lane in which the host vehicle V1 travels.

  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 communication device 40 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 detection reference setting unit 34, and a detection control unit 35. Below, each structure is demonstrated.

  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.

  The alignment unit 32 sequentially inputs the bird's-eye view image data obtained by the viewpoint conversion of the viewpoint conversion unit 31 and aligns the positions of the inputted bird's-eye view 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 V1, and FIG. 4B is an image showing the outline of the alignment.

As shown in FIG. 4 (a), the host vehicle V1 of the current time is located in P _1, one unit time before the vehicle V1 is located in the P _{1 '.} Further, there is a parallel running state with the vehicle V1 is located is adjacent vehicle V2 laterally after the vehicle V1, located in P ₂ adjacent vehicle V2 is the current time, one unit time before the adjacent vehicle V2 is P ₂ Suppose it is located at '. Furthermore, it is assumed that the host vehicle V1 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 view image PB _t at the current time is as shown in Figure 4 (b). In the bird's-eye view 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, the adjacent vehicle V2 (position P ₂₎ is tilting occurs. Similarly, for the bird's-eye view image PB _{t-1 one} hour before, the white line drawn on the road surface has a rectangular shape and is in a state of being relatively accurately viewed in plan, but the adjacent vehicle V2 (position P _2). ') Will fall 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 view 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 V1 shown in FIG. 4 (a). It is determined based on the time until the time.

In addition, after the alignment, the alignment unit 32 calculates the difference between the bird's-eye view images PB _t and PB _t−1 and generates data of the difference image PD _t . Here, in the present embodiment, the alignment unit 32 converts the difference between the pixel values of the bird's-eye view images PB _t and PB _t−1 to an absolute value in order to cope with a change in the illumination environment, and the absolute value is a predetermined value. By setting the pixel value of the difference image PD _t to “1” when the threshold value is equal to or greater than the threshold th, and setting the pixel value of the difference image PD _t to “0” when the absolute value is less than the predetermined threshold th, Data of the difference image PD _t as shown on the right side of FIG. 4B can be generated.

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 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.

In generating the difference waveform, the three-dimensional object detection unit 33 sets a detection region in the difference image PD _t . The three-dimensional object detection device 1 of the present example is intended to calculate a movement distance for an adjacent vehicle that may be contacted when the host vehicle V1 changes lanes. For this reason, in this example, as shown in FIG. 2, rectangular detection areas A1, A2 are set on the rear side of the host vehicle V1. Such detection areas A1, A2 may be set from a relative position with respect to the host vehicle V1, or may be set based on the position of the white line. When setting the position of the white line as a reference, the three-dimensional object detection device 1 may use, for example, an existing white line recognition technique.

  Further, as shown in FIG. 2, 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 V1 side as the ground lines L1 and L2. 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 original adjacent vehicle V2 is not too large, and there is no problem in practical use.

FIG. 5 is a schematic diagram illustrating how the three-dimensional object detection unit 33 generates a differential waveform. 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.

Specifically, first three-dimensional object detection unit 33 defines a line La on the direction the three-dimensional object collapses on data of the difference image PD _t. Then, the three-dimensional object detection unit 33 counts the number of difference pixels DP indicating a predetermined difference on the line La. In the present embodiment, the difference pixel DP indicating the predetermined difference is expressed by the pixel value of the difference image PD _t as “0” and “1”, and the pixel indicating “1” is counted as the difference pixel DP. .

  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 left-right axis in the right diagram of FIG. 5 is determined and plotted as the number of counts at the intersection CP.

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.

Here, the difference pixel PD on the data of the difference image PD _t is a pixel that has changed in the images at different times, in other words, a location 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. Thus, the relative position of the three-dimensional object with respect to the host vehicle can be detected by generating the differential waveform DW _t . For example, the three-dimensional object detection unit 33 can detect the peak position of the differential waveform DW _t as the relative position of the three-dimensional object.

As described above, the differential waveform DW _t is an aspect of pixel distribution information indicating a predetermined luminance difference, and the “pixel distribution information” in the present embodiment is obtained when the captured image is converted into a bird's-eye view image. It can be positioned as information indicating the distribution state of “pixels having a luminance difference equal to or greater than a predetermined threshold” detected along the direction in which the three-dimensional object falls. That is, in the bird's eye view image obtained by the viewpoint conversion unit 31, the three-dimensional object detection unit 33 distributes pixels whose luminance difference is greater than or equal to a predetermined threshold along the direction in which the three-dimensional object falls when the viewpoint is converted into the bird's eye view image. By detecting the three-dimensional object, the relative position of the three-dimensional object is detected based on the detected pixel distribution information.

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 difference waveform DW _t , the three-dimensional object detection unit 33 detects the adjacent vehicle existing in the adjacent lane based on the generated difference waveform DW _t . First, three-dimensional object detection unit 33 calculates the moving distance in comparison with the differential waveform DW _t-1 of the previous differential waveform DW _t and a time instant at the current time. 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 adjacent vehicle 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 ^* . As described above, in this embodiment, even if the offset amount varies, it is possible to calculate a more accurate movement distance from the maximum value. The moving distance τ ^* is a relative moving distance of the adjacent vehicle with respect to the own vehicle. 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.

Thus, in the present embodiment, a one-dimensional waveform is obtained by calculating the moving distance of the three-dimensional object 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. Thus, the movement distance is calculated from the offset amount of the information, and the calculation cost can be suppressed in calculating the movement distance. In addition, by dividing the differential waveform DW _t generated at different times into a plurality of small regions DW _{t1 to} DW _tn , a plurality of waveforms representing the respective locations of the three-dimensional object can be obtained. Since the offset amount can be obtained for each of the positions, and the movement distance can be obtained from a plurality of offset amounts, the calculation accuracy of the movement distance can be improved. Further, in the present embodiment, by calculating the moving distance of the three-dimensional object from the time change of the differential waveform DW _t including the information in the height direction, compared with a case where attention is paid only to one point of movement, Since the detection location before the time change and the detection location after the time change are specified including information in the height direction, it is likely to be the same location in the three-dimensional object, and the movement distance is calculated from the time change of the same location, and the movement Distance calculation accuracy can be improved.

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 above disclosure.

  In the present embodiment, the three-dimensional object detection unit 33 obtains the moving speed of the host vehicle V1 (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 ignores the offset amount corresponding to the stationary object among the maximum values of the histogram and calculates the moving distance of the adjacent vehicle.

  FIG. 9 is a diagram illustrating another example of a histogram obtained by the three-dimensional object detection unit 33. When there is a stationary object in addition to the adjacent vehicle 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. Thereby, the situation where the calculation accuracy of the moving distance of a solid object falls by a stationary object can be prevented.

  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 adjacent vehicles within the angle of view of the camera 10. However, it is extremely rare that a plurality of adjacent vehicles exist in the detection areas A1 and A2. For this reason, the three-dimensional object detection unit 33 stops calculating the movement distance. Thereby, in the present embodiment, it is possible to prevent a situation in which an erroneous movement distance having a plurality of maximum values is calculated.

  Furthermore, the three-dimensional object detection unit 33 calculates the relative movement speed of the three-dimensional object with respect to the host vehicle by differentiating the calculated relative movement distance of the three-dimensional object with respect to time, and the vehicle speed sensor The absolute moving speed of the three-dimensional object is calculated by adding the vehicle speed detected by the vehicle 20.

After the generation of the difference waveform DW _t , the three-dimensional object detection unit 33 detects the adjacent vehicle existing in the adjacent lane based on the generated difference waveform DW _t . Here, FIG. 10 is a diagram for explaining a method of determining other vehicles existing in the adjacent lane, and shows an example of the difference waveform DW _t and a threshold value α for detecting the adjacent vehicle existing in the adjacent lane. Yes. For example, as illustrated in FIG. 10, the three-dimensional object detection unit 33 determines whether or not the peak of the generated difference waveform DW _t is _equal to or greater than a predetermined threshold α, and the peak of the difference waveform DW _t is _{equal to} the predetermined threshold α. If it is above, it is determined that the detected three-dimensional object is an adjacent vehicle existing in the adjacent lane, and is detected by the three-dimensional object detection unit 33 when the peak of the differential waveform DW _t is not equal to or greater than the predetermined threshold value α. It is determined that the three-dimensional object is not an adjacent vehicle existing in the adjacent lane.

  In the present embodiment, the threshold value α is set by the detection reference setting unit 34 shown in FIG. Here, for example, at night, when the own vehicle overtakes an adjacent vehicle and the light environment around the own vehicle becomes dark, the exposure sensitivity of the camera 10 increases the imaging sensitivity, and noise having a predetermined luminance (of the camera 10 Noise due to dark current characteristics) may occur. The detection reference setting unit 34 sets a threshold value α to prevent such an image caused by noise from being erroneously detected as an image of an adjacent vehicle.

Specifically, the detection reference setting unit 34 detects the luminance of the difference image PD _t corresponding to the detection areas A1 and A2, and is caused by dark current characteristics in the detection areas A1 and A2, as shown in FIG. An area having a luminance value included in the luminance value band where noise is likely to occur is specified as the specific luminance area. Then, the detection reference setting unit 34 detects the degree of darkness of the difference image PD _t corresponding to the specific luminance area, and the darkness degree of the difference image PD _t corresponding to the detection areas A1 and A2 is larger (the darker). The threshold value α in the specific luminance region is set to a high value. FIG. 11 is a graph showing the relationship between the luminance and the threshold value α.

The detection method of the darkness degree of the difference image PD _t is not particularly limited, in the present embodiment, the detection reference setting unit 34, from the size of the extraction reference value can be extracted more than a predetermined amount of edge components, specific brightness it is possible to detect the darkness degree of the difference image PD _t corresponding to the region. For example, the detection reference setting unit 34 sets the extraction reference value to a predetermined value ts as shown in FIG. Then, the detection reference setting unit 34 extracts an edge component exceeding the changed extraction reference value from the difference image PD _t corresponding to the specific luminance region while changing the set extraction reference value ts to a small value, and outputs a predetermined amount. The extraction reference value ts ′ (the largest extraction reference value ts ′ among the extraction reference values from which a predetermined amount or more of edge components can be extracted) is identified.

Here, as described above, the darker the difference image PD _t corresponding to the specific luminance region, the higher the imaging sensitivity of the camera 10 due to the exposure control of the camera 10, thereby causing the dark current characteristics of the camera 10. Noise is likely to occur, and an edge component based on such a noise image is likely to be detected. Further, the darker the difference image PD _t corresponding to the specific luminance region, the higher the intensity of the edge component based on the noise caused by the dark current characteristics of the camera 10. Therefore, when an edge component exceeding the extraction criterion is extracted from the difference image PD _t corresponding to the specific luminance region, an edge component of a predetermined amount or more can be extracted as the difference image PD _t corresponding to the specific luminance region is darker. The detection reference value increases. Therefore, the detection reference setting unit 34 detects the degree of darkness of the difference image PD _t corresponding to the specific luminance region as the extraction reference value from which the edge component of a predetermined amount or more can be extracted is larger.

Then, the detection criterion setting unit 34 increases the change amount Δα of the threshold value α illustrated in FIG. 11 and increases the threshold value α in the specific luminance region as the darkness degree of the difference image PD _t corresponding to the specific luminance region is higher. Set to. Then, the three-dimensional object detection unit 33 determines whether or not the detected three-dimensional object is an adjacent vehicle existing in the adjacent lane using the threshold value α set in the detection reference setting unit 34 in this way. As described above, the higher the degree of darkness of the differential image PD _t corresponding to the specific luminance region, the larger the change amount Δα of the threshold α shown in FIG. 11 is set to a higher value. Thus, it is possible to prevent an image due to noise caused by the dark current characteristics of the camera 10 from being erroneously detected as an image of an adjacent vehicle.

  Returning to FIG. 3, the detection control unit 35 determines whether or not the own vehicle has passed the three-dimensional object based on the temporal change in the relative position of the three-dimensional object detected by the three-dimensional object detection unit 33. When it is determined that the object is overtaken, as shown in FIG. 13C, the three-dimensional object is detected in the detection target area excluding the shift area behind the traveling direction in the detection areas A1 and A2. The detection unit 33 is controlled. FIG. 13 is a diagram for explaining a detection control method by the detection control unit 35.

  FIG. 13 illustrates a scene in which the host vehicle V1 is overtaking the adjacent vehicle V2 existing in the adjacent lane. Moreover, in FIG. 13, since the adjacent vehicle V2 is irradiating the headlight at night, the scene where the light of the headlight of the adjacent vehicle V2 is reflected on the road surface ahead of the adjacent vehicle V2 is illustrated. Yes. When the host vehicle V1 is traveling at a higher speed than the adjacent vehicle V2, as shown in FIG. 13A, the adjacent vehicle V2 (three-dimensional object) traveling in front of the host vehicle V1 is shown in FIG. As shown to B), it detects within the detection area A1 of the back side of the own vehicle. When the adjacent vehicle V2 (three-dimensional object) is detected in the detection area A1, the detection control unit 35 determines whether the own vehicle V1 has passed the adjacent vehicle V2 due to the position change of the adjacent vehicle V2 (three-dimensional object) with respect to the own vehicle V1. Judge whether or not.

  For example, the detection control unit 35 calculates the relative moving speed of the host vehicle V1 relative to the adjacent vehicle V2 (three-dimensional object) based on the temporal change in the relative position of the three-dimensional object detected by the three-dimensional object detection unit 33. When the relative movement speed of the host vehicle V1 is equal to or higher than a predetermined speed (for example, a speed greater than +0 km / h), it can be determined that the host vehicle V1 has overtaken the adjacent vehicle V2. Alternatively, the detection control unit 35 can detect the adjacent vehicle V2 (three-dimensional object) in the detection area A1 when the relative movement speed of the host vehicle V1 is equal to or higher than a predetermined speed (for example, a speed greater than +0 km / h). When it disappears, you may judge that the own vehicle V1 overtakes the adjacent vehicle V2. The detection control unit 35 acquires the relative movement speed of the host vehicle V1 with respect to the adjacent vehicle V2 (three-dimensional object) calculated by the three-dimensional object detection unit 33, and determines whether the host vehicle V1 has passed the adjacent vehicle V2. It is good also as composition to do.

In addition, the detection control unit 35 determines from the own vehicle V1 (camera 10) to the adjacent vehicle (three-dimensional object) based on the relative movement speed of the adjacent vehicle V2 (three-dimensional object) with respect to the own vehicle V1 detected by the three-dimensional object detection unit 33. The rear distance from the host vehicle V1 (camera 10) to the adjacent vehicle (three-dimensional object) is equal to or greater than a predetermined distance, and the adjacent vehicle V2 (three-dimensional object) cannot be detected in the detection area A1. In this case, it may be determined that the host vehicle V1 has overtaken the adjacent vehicle V2 (three-dimensional object). Alternatively, the detection control unit 35 moves the peak position of the differential waveform DW _t detected by the three-dimensional object detection unit 33 from the front to the back of the detection areas A1 and A2, and generates a differential waveform from the host vehicle V1 (camera 10). When the rear distance to the peak position of DW _t is _equal to or greater than a predetermined distance and the adjacent vehicle V2 (three-dimensional object) cannot be detected in the detection areas A1 and A2, the host vehicle V1 is adjacent to the adjacent vehicle V2 (three-dimensional object). It is good also as a structure judged as having overtaken. Thus, in this embodiment, since it can be determined that the host vehicle V1 has overtaken the adjacent vehicle V2 (three-dimensional object) based on the image captured by the camera 10, the configuration of the three-dimensional object detection device 1 is simplified. can do.

When the detection control unit 35 determines that the host vehicle V1 has overtaken the adjacent vehicle V2, the detection control unit 35, as shown in FIG. 13C, the mask region behind the traveling direction in the detection region A1, and the detection region A1. The detection object area ahead of the traveling direction is set, and the three-dimensional object detection unit 33 is made to detect the three-dimensional object only in the detection object area excluding the mask area from the detection area A1. For example, when generating the differential waveform DW _t , the detection control unit 35 sets the count of the frequency distribution in the mask region to zero and generates the differential waveform DW _t from only the differential image PD _t corresponding to the detection target region. By controlling the three-dimensional object detection unit 33, it is possible to cause the three-dimensional object detection unit 33 to detect the three-dimensional object only in the detection target region without detecting the three-dimensional object in the mask region.

  As a result, for example, as shown in FIG. 13C, the host vehicle V1 has overtaken the adjacent vehicle V2, so that the adjacent vehicle V2 does not exist in the detection area A1, but the adjacent vehicle V2 It is effective that the light emitted from the headlight is reflected on the road surface in the mask area in the detection area A1, and the image of the light reflected on the road surface in the mask area in the detection area A1 is erroneously detected as a three-dimensional object. Can be prevented.

  In the present embodiment, the detection area A1 is not limited to the case where the light of the headlight of the adjacent vehicle V2 that the host vehicle V1 has overtaken is irradiated. For example, the shadow of the adjacent vehicle V2 that the host vehicle V1 has overtaken is detected. Even when projected onto A1 or when an image of the adjacent vehicle V2 overtaken by the host vehicle V1 is reflected in a puddle in the detection area A1, erroneous detection of a three-dimensional object can be effectively prevented.

  In the present embodiment, the detection control unit 35 passes the adjacent vehicle (three-dimensional object) and sets a detection target area excluding the mask area from the detection areas A1 and A2, and then a predetermined set time. Only the detection target area is set. Then, after the set time elapses, the detection control unit 35 gradually expands the set detection target region, and finally detects the three-dimensional object in the three-dimensional object detection unit 33 in the entire detection regions A1 and A2. Make it.

  Furthermore, the detection control unit 35 changes the size of the detection target area to be set (the width with respect to the traveling direction) and the set time according to the following criteria. Specifically, when the detection control unit 35 determines that the host vehicle has overtaken the three-dimensional object, the detection control unit 35 acquires the relative movement speed of the host vehicle with respect to the three-dimensional object from the three-dimensional object detection unit 33, and the host vehicle with respect to the acquired three-dimensional object. The detection target area is set to a wider range as the relative movement speed of is higher. Similarly, the detection control unit 35 sets the set time for setting the detection target region to a shorter time as the relative movement speed of the host vehicle with respect to the three-dimensional object increases. Thereby, for example, when the own vehicle overtakes the adjacent vehicle, the relative movement speed of the own vehicle with respect to the adjacent vehicle is slow, and therefore the light of the headlight of the adjacent vehicle overtaken by the own vehicle is irradiated to the detection areas A1 and A2. Even if the time is long and the headlight light image of the adjacent vehicle overtaken by the host vehicle is likely to be erroneously detected as the adjacent vehicle, the headlight light image of the adjacent vehicle overtaken by the host vehicle is displayed. It is possible to effectively prevent erroneous detection as an adjacent vehicle.

  In addition, the detection control unit 35 acquires the current weather information of the area where the host vehicle travels from the communication device 40, and determines whether or not the current weather is rainy. And when the detection control part 35 judges that the present weather is rainy, compared with the case where it judges that the present weather is not rainy, it sets a detection object area | region to a narrow range. Similarly, the detection control unit 35, when it is determined that the current weather in the area where the host vehicle is traveling is rainy, is compared with the case where it is determined that the current weather is not rainy. Set the setting time to a long time. Thereby, the detection control unit 35 is raining and the light of the headlight of the adjacent vehicle over which the own vehicle has overtaken is easily reflected on the road surface in the detection areas A1 and A2, or the adjacent vehicle over which the own vehicle has overtaken. Even when the image is likely to appear in the puddles in the detection areas A1 and A2, erroneous detection of the adjacent vehicle can be effectively prevented.

  In addition, for example, when the own vehicle overtakes two adjacent vehicles in succession and the detection control unit 35 determines that the own vehicle has overtaken the three-dimensional object (first adjacent vehicle), the detection control unit 35 Another three-dimensional object (second adjacent vehicle) that is different from the three-dimensional object that is the object of determination is detected ahead of the traveling direction than the three-dimensional object (first adjacent vehicle) that is the object of overtaking determination. In such a case, the detection target area is set to a wider range as compared to the case where another solid object (second adjacent vehicle) different from the solid object that is the target of the overtaking determination is not detected. Similarly, the detection control unit 35, when it is determined that the host vehicle has overtaken the three-dimensional object (first adjacent vehicle), another three-dimensional object that is different from the three-dimensional object subjected to the overtaking determination ( When the second adjacent vehicle) is detected ahead of the three-dimensional object (first adjacent vehicle) that is the target of the overtaking determination, the set time for setting the detection target region is set to a short time. Set to. Thus, for example, in a situation where the host vehicle continuously overtakes two adjacent vehicles, the second vehicle is set when the mask region and the detection target region are set by overtaking the first adjacent vehicle. The adjacent vehicle enters the mask area set in the detection areas A1 and A2, and the second adjacent vehicle cannot be detected effectively.

Next, the adjacent vehicle detection process according to the present embodiment will be described. FIG. 14 is a flowchart illustrating the adjacent vehicle detection process of the first embodiment. As shown in FIG. 14, first, the computer 30 acquires captured image data from the camera 10 (step S <b> 101), and the viewpoint conversion unit 31 acquires the bird's-eye view image PB based on the acquired captured image data. Data of _t is generated (step S102).

Next, the alignment unit 32 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 before, and generates data of the difference image PD _t (step S103). . Then, three-dimensional object detection unit 33, from the data of the difference image PD _t, pixel value by counting the number of difference pixel DP "1", to generate a difference waveform DW _t (step S104).

Then, the detection criterion setting unit 34 sets a threshold value α that is a detection criterion for detecting a three-dimensional object (step S106). Further, the detection reference setting unit 34 specifies the luminance included in the luminance value band in which disturbance due to the dark current characteristic of the camera 10 is likely to occur among the image regions on the difference image PD _t corresponding to the detection regions A1 and A2. It is determined whether or not a luminance area exists (step S107). When the specific luminance area exists in the detection areas A1 and A2, as shown in FIG. 11, the detection reference setting unit 34 changes the threshold value α corresponding to the specific luminance area to a high value. Specifically, the detection reference setting unit 34, as shown in FIG. 12, while changing the extraction criterion for extracting an edge component from the difference image PD _t, by extracting the edge component from the difference image PD _t Then, an extraction criterion value that can extract an edge component of a predetermined amount or more is identified, and the threshold value α corresponding to the specific luminance region is changed to a higher value as the identified extraction criterion value is higher.

Then, 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 threshold value α set in step S105 or changed in step S107 (step S108). When the peak of the difference waveform DW _t is not equal to or greater than the 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. For this reason, when it is determined that the peak of the difference waveform DW _t is not equal to or greater than the threshold value α (step S108 = No), the three-dimensional object detection unit 33 determines that there is no three-dimensional object and no other vehicle exists (step S108). S117). And it returns to step S101 and repeats the process shown in FIG.

On the other hand, if it is determined that the peak of the difference waveform DW _t is equal to or greater than the threshold value α (step S108 = Yes), the three-dimensional object detection unit 33 determines that a three-dimensional object exists in the adjacent lane, and proceeds to step S109. The three-dimensional object detection unit 33 divides the differential waveform DW _t into a plurality of small areas DW _{t1 to} DW _tn . Next, the three-dimensional object detection unit 33 performs weighting for each of the small areas DW _{t1 to} DW _tn (Step S110), calculates an offset amount for each of the small areas DW _{t1 to} DW _tn (Step S111), and adds the weight. A histogram is generated (step S112).

  Then, the three-dimensional object detection unit 33 calculates a relative movement speed of the three-dimensional object relative to the own vehicle by performing a relative movement distance that is a movement distance of the three-dimensional object relative to the own vehicle based on the histogram, and time-differentiating the calculated relative movement distance. At the same time (step S113), the vehicle speed detected by the vehicle speed sensor 20 is added to the calculated relative movement speed to calculate the absolute movement speed of the three-dimensional object relative to the host vehicle (step S114).

  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 is +60 km / h or less (step S115). When both are satisfied (step S115 = Yes), the three-dimensional object detection unit 33 determines that the detected three-dimensional object is an adjacent vehicle existing in the adjacent lane, and an adjacent vehicle exists in the adjacent lane (step S116). Then, the process shown in FIG. 14 ends. On the other hand, when either one is not satisfied (step S115 = No), the three-dimensional object detection unit 33 determines that there is no adjacent vehicle in the adjacent lane (step S117). And it returns to step S101 and repeats the process shown in FIG.

  In the present embodiment, the rear side of the host vehicle is set as the detection areas A1 and A2, and emphasis is placed on whether or not there is a possibility of contact when the host vehicle changes lanes. For this reason, the process of step S115 is performed. That is, assuming that the system according to the present embodiment is operated on a highway, if the speed of the adjacent vehicle is less than 10 km / h, even if the adjacent vehicle exists, the host vehicle is required to change the lane. Because it is located far behind, there are few problems. Similarly, when the relative moving speed of the adjacent vehicle to the own vehicle exceeds +60 km / h (that is, when the adjacent vehicle is moving at a speed higher than 60 km / h than the speed of the own vehicle), when changing the lane Is less likely to be a problem because it is moving in front of the host vehicle. For this reason, it can be said that the adjacent vehicle which becomes a problem at the time of lane change is judged in step S115.

  In step S115, it is determined whether the absolute moving speed of the adjacent vehicle is 10 km / h or more and the relative moving speed of the adjacent vehicle with respect to the own vehicle is +60 km / h or less. 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 that the stationary object is determined to be an adjacent vehicle. Further, depending on the noise, the relative speed of the adjacent vehicle to the host vehicle may be detected as 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 process in step S115, it may be determined that the absolute moving speed of the adjacent vehicle is not negative or not 0 km / h. Further, in this embodiment, since an emphasis is placed on whether or not there is a possibility of contact when the host vehicle changes lanes, a warning sound is sent to the driver of the host vehicle when an adjacent vehicle is detected in step S116. Or a display corresponding to a warning may be performed by a predetermined display device.

  Next, the detection control process according to the first embodiment will be described. In the detection control process, when the host vehicle overtakes an adjacent vehicle, as shown in FIG. 13C, the area behind the detection areas A1 and A2 is set as a mask area, and the detection areas A1 and A2 This is a process of setting a front area as a detection target area and causing the three-dimensional object control unit 33 to detect a three-dimensional object only in the detection target area without detecting the three-dimensional object in the mask area. FIG. 15 is a flowchart showing the detection control process according to the first embodiment. In addition, the detection control process demonstrated below can be performed in parallel with the adjacent vehicle detection process shown in FIG.

  First, in step S201, the detection control unit 35 determines whether an adjacent vehicle is detected in the detection areas A1 and A2 based on the determination result of the adjacent vehicle determination process shown in FIG. When the adjacent vehicle is detected in the detection areas A1 and A2, the process proceeds to step S202. On the other hand, when the adjacent vehicle is not detected in the detection areas A1 and A2, step S201 is repeated.

  For example, in the example shown in FIG. 13A, since the adjacent vehicle V2 exists in front of the host vehicle V1, the adjacent vehicle V2 is not detected in the detection area A1, and the detection control unit 35 performs step S201. repeat. Then, as shown in FIG. 13B, when the own vehicle V1 moves to the front of the adjacent vehicle V2 and the adjacent vehicle V2 is detected in the detection area A1, the process proceeds to step S202.

  In step S202, the detection control unit 35 determines whether the host vehicle has overtaken an adjacent vehicle. In the present embodiment, the detection control unit 35, for example, when the relative movement speed of the host vehicle V1 with respect to the adjacent vehicle V2 is equal to or higher than a predetermined speed and the adjacent vehicle V2 is not detected in the detection areas A1 and A2. It can be determined that the host vehicle V1 has overtaken the adjacent vehicle V2.

  For example, as a result of the host vehicle V1 traveling at a predetermined speed or higher with respect to the adjacent vehicle V2, as shown in FIG. 13B, from the state in which the adjacent vehicle V2 is detected in the detection area A1, FIG. ), When the adjacent vehicle V2 deviates from the detection area A1 and the adjacent vehicle V2 is no longer detected in the detection area A1, the detection control unit 35 determines that the relative movement speed of the host vehicle V1 with respect to the adjacent vehicle V2 is It is determined that the host vehicle V1 has overtaken the adjacent vehicle V2 by detecting that the adjacent vehicle V2 is no longer detected in the detection area A1 at a speed equal to or higher than the predetermined speed.

  In step S <b> 203, the detection control unit 35 sets a detection target area excluding a mask area behind the traveling direction in the detection area. For example, as shown in FIG. 13C, when the host vehicle V1 overtakes the adjacent vehicle V2, the detection control unit 35 sets a region behind the traveling direction in the detection region as a mask region, and starts from the detection region. An area excluding the mask area is set as a detection target area.

  In addition, when setting the detection target region, the detection control unit 35 determines the range of the detection target region to be set according to the following criteria. Specifically, the detection control unit 35 sets the detection target area to be wider as the relative movement speed of the host vehicle with respect to the three-dimensional object is higher. Furthermore, when the current weather is rainy, the detection control unit 35 sets the detection target region to be a narrower range than when the current weather is not rainy. In addition, the detection control unit 35 detects when a three-dimensional object different from the three-dimensional object subjected to the overtaking determination is detected ahead of the traveling direction than the three-dimensional object subjected to the overtaking determination. Set the target area to be wide.

  Next, in step S204, the detection control unit 35 sets a set time for setting the detection target region. Specifically, the detection control unit 35 sets a set time for setting the detection target region according to the following criteria. Specifically, the detection control unit 35 can set the set time for setting the detection target region to a shorter time as the relative movement speed of the host vehicle with respect to the three-dimensional object is higher. Further, when the current weather is rainy, the detection control unit 35 can set the set time for setting the detection target region to be longer than when the current weather is not rainy. In addition, the detection control unit 35 detects when a three-dimensional object different from the three-dimensional object subjected to the overtaking determination is detected ahead of the traveling direction than the three-dimensional object subjected to the overtaking determination. The set time for setting the target area can be set to a short time.

  In step S205, the detection control unit 35 causes the three-dimensional object detection unit 33 to detect a three-dimensional object only in the detection target region set in step S203. As a result, the three-dimensional object detection unit 33 does not detect the three-dimensional object in the mask area, so that the host vehicle overtakes the adjacent vehicle, so the light of the headlight of the adjacent vehicle projected on the mask area or the adjacent vehicle It can be effectively prevented that the shadow of the vehicle or the image of the adjacent vehicle reflected in the puddle is erroneously detected as the adjacent vehicle.

Next, in step S206, the detection control unit 35 determines whether or not the peak of the differential waveform DW _t is greater than or equal to a predetermined value α ′. Here, the predetermined value α ′ is a value by which it can be determined that no three-dimensional object exists in the detection areas A1 and A2 when the peak of the differential waveform DW _t is less than the predetermined value α ′. Is a value determined in advance. If there is a peak of the differential waveform DW _t that is greater than or equal to the predetermined value α ′, it is determined that there is an adjacent vehicle that the host vehicle has overtaken in the detection areas A1 and A2, and the process proceeds to step S208. On the other hand, if there is no peak of the difference waveform DW _t greater than or equal to the predetermined value, it is determined that there is no adjacent vehicle over which the host vehicle has passed in the detection areas A1 and A2 because the host vehicle has passed the adjacent vehicle. Then, the process proceeds to step S207.

  In step S207, since it is determined that there is no adjacent vehicle that the host vehicle has overtaken in the detection areas A1 and A2, the detection control unit 35 performs processing for canceling the mask area. Accordingly, the detection control unit 35 causes the three-dimensional object detection unit 33 to detect the three-dimensional object in the entire detection areas A1 and A2 in the subsequent step S210.

On the other hand, if it is determined in step S206 that there is a peak of the differential waveform DW _t greater than or equal to the predetermined value α ′, the process proceeds to step S208. In step S208, the detection control unit 35 determines whether or not the set time set in step S204 has elapsed. If the set time has elapsed, the process proceeds to step S209, where the mask area is gradually reduced, and finally the mask area is released. Accordingly, the detection control unit 35 causes the three-dimensional object detection unit 33 to detect the three-dimensional object in the entire detection areas A1 and A2 in the subsequent step S210.

As described above, in the first embodiment, two captured images at different times are converted into bird's-eye view images, a difference image PD _t is generated based on the difference between the two bird's-eye view images, and the difference image PD _When the adjacent vehicle existing in the adjacent lane is detected from the difference data on _t , it is determined whether or not the host vehicle has passed the adjacent vehicle. Then, when the host vehicle overtakes the adjacent vehicle, as shown in FIG. 13C, the detection target area excluding the shift area behind the traveling direction is set in the detection areas A1 and A2, and the set detection is performed. A solid object is detected in the target area. Thereby, in this embodiment, since a solid object is not detected in a mask area | region but a solid object is detected only in a detection object area | region, as shown to FIG.13 (C), for example, the own vehicle V1 is an adjacent vehicle. By overtaking V2, the light emitted from the headlight of the adjacent vehicle V2 is reflected on the road surface in the mask area in the detection area A1 even though the adjacent vehicle V2 does not exist in the detection area A1. In the case where the vehicle is over, the shadow of the adjacent vehicle overtaken by the own vehicle is projected on the detection areas A1 and A2, or the image of the adjacent vehicle overtaken by the own vehicle is reflected in the puddle in the detection areas A1 and A2. In this case, erroneous detection of these images as adjacent vehicles can be effectively prevented.

<< Second Embodiment >>
Next, the three-dimensional object detection device 1a according to the second embodiment will be described. As shown in FIG. 16, the three-dimensional object detection device 1 a according to the second embodiment includes a computer 30 a instead of the computer 30 of the first embodiment, except that it operates as described below. This is the same as in the first embodiment. Here, FIG. 16 is a block diagram showing details of the computer 30a according to the second embodiment.

  As shown in FIG. 16, the three-dimensional object detection device 1a according to the second embodiment includes a camera 10 and a computer 30a. The computer 30a includes a viewpoint conversion unit 31, a luminance difference calculation unit 36, and an edge line detection unit. 37, a three-dimensional object detection unit 33a, a detection reference setting unit 34a, and a detection control unit 35. Below, each structure of the solid-object detection apparatus 1a which concerns on 2nd Embodiment is demonstrated.

  FIG. 17 is a diagram illustrating an imaging range and the like of the camera 10 of FIG. 16, FIG. 17A is a plan view, and FIG. 17B is a perspective view in real space on the rear side from the host vehicle V1. Show. As shown in FIG. 17A, the camera 10 has a predetermined angle of view a, and images the rear side from the host vehicle V1 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 V1 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 V1 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 V1 travels contacts the ground. In the present embodiment, the object is to detect adjacent vehicles V2 and the like (including two-wheeled vehicles) traveling in the left and right lanes adjacent to the lane of the host vehicle V1 on the rear side of the host vehicle V1. For this reason, a distance d1 which is a position to be the ground lines L1, L2 of the adjacent vehicle V2 is determined from a distance d11 from the own vehicle V1 to the white line W and a distance d12 from the white line W to a position where the adjacent vehicle V2 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 30a recognizes the position of the white line W with respect to the host vehicle V1 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 adjacent vehicle V2 travels (distance d12 from the white line W) and the position where the host vehicle V1 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 in the vehicle traveling direction from the rear end portion of the host vehicle V1. 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 adjacent vehicle V2 or the like, the distance d3 is set to a length including the adjacent vehicle V2.

  As shown in FIG. 17B, the distance d4 is a distance indicating a height that is set to include a tire such as the adjacent vehicle V2 in the real space. The distance d4 is a length shown in FIG. 17A in the bird's-eye view image. Note that the distance d4 may be a length that does not include a lane that is further adjacent to the left and right lanes in the bird's-eye view image (that is, the adjacent lane that is adjacent to two lanes). If the lane adjacent to the two lanes is included from the lane of the own vehicle V1, there is an adjacent vehicle V2 in the adjacent lane on the left and right of the own lane that is the lane in which the own vehicle V1 is traveling. This is because it becomes impossible to distinguish whether there is an adjacent vehicle on the lane.

  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. 17B, the detection areas A1 and A2 are true squares (rectangles) in real space on the rear side from the host vehicle V1.

  Returning to FIG. 16, the viewpoint conversion unit 31 inputs captured image data of a predetermined region obtained by imaging with 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 36 calculates a luminance difference with respect to the bird's-eye view image data subjected to the 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 calculation unit 36 calculates a brightness difference between two pixels in the vicinity of each position. The luminance difference calculation unit 36 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.

  Here, a specific method for setting two vertical virtual lines will be described. The brightness difference calculating unit 36 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 36 continuously obtains the luminance difference between the point on the first vertical imaginary line and the 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 36 will be described in detail.

  As shown in FIG. 18A, the luminance difference calculation unit 36 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 36 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 36 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 36 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. 18B in the real space. As apparent from FIG. 18B, 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 36 calculates the 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. In particular, in the second embodiment, in order to detect a three-dimensional object existing in the detection areas A1 and A2, a vertical virtual line is set as a line segment extending in the vertical direction in the real space with respect to the bird's-eye view image, 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. For this reason, the edge line detection unit 37 shown in FIG. 16 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. 19 is a diagram illustrating a detailed operation of the luminance difference calculation unit 36. FIG. 19A illustrates a bird's-eye view image in a bird's-eye view state, and FIG. 19B illustrates a state illustrated in FIG. 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. 19, the luminance difference is calculated in the same procedure for the detection area A2.

  When the adjacent vehicle V2 is reflected in the captured image captured by the camera 10, the adjacent vehicle V2 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 region B1 in FIG. 19A in FIG. 19B, it is assumed that the attention line La is set on the rubber part of the tire of the adjacent vehicle V2 on the bird's-eye view image. In this state, the luminance difference calculation unit 36 first sets a 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 1a according to the present embodiment, the reference line Lr is set at a position separated from the attention line La by 10 cm in the real space. Thereby, the reference line Lr is set on the wheel of the tire of the adjacent vehicle V2, which is separated from the rubber of the tire of the adjacent vehicle V2, for example, by 10 cm, on the bird's eye view image.

  Next, the luminance difference calculation unit 36 sets a plurality of attention points Pa1 to PaN on the attention line La. In FIG. 19B, 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 36 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 36 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 36 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. The luminance difference calculation unit 36 calculates, for example, a luminance difference between the first attention point Pa1 and the first reference point Pr1, and a luminance difference between the second attention point Pa2 and the second reference point Pr2. Will be calculated. Thereby, the luminance difference calculation unit 36 continuously obtains the luminance difference along the attention line La and the reference line Lr. That is, the luminance difference calculation unit 36 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 36 repeatedly executes the 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 36 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 36 sets a line that has been the reference line Lr in the previous process as the attention line La, sets the reference line Lr for the attention line La, and sequentially obtains the luminance difference. It will be.

  As described above, in the second embodiment, the edge extending in the vertical direction is obtained by calculating the luminance difference from the attention point Pa on the attention line La and the reference point Pr on the reference line Lr that are substantially the same height in the real space. It is possible to clearly detect a luminance difference in the case where there is. Also, in order to compare the brightness of 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 to a bird's-eye view image, The detection process is not affected, and the detection accuracy of the three-dimensional object can be improved.

  Returning to FIG. 16, the edge line detection unit 37 detects an edge line from the continuous luminance difference calculated by the luminance difference calculation unit 36. For example, in the case illustrated in FIG. 19B, 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 37 can 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 37 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 predetermined threshold, 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. Indicates. 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 luminance threshold 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”.

Next, the edge line detection unit 37 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 part 37 calculates | requires the sum total about the continuity c of all the attention points Pa on attention line La. The edge line detection unit 37 normalizes the continuity c by dividing the obtained sum of continuity c by the number N of points of interest Pa. Then, the edge line detection unit 37 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.

That is, the edge line detection unit 37 determines whether or not the attention line La is an edge line based on Equation 3 below. Then, the edge line detection unit 37 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

  As described above, in the second embodiment, 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 the attribute along the attention line La is attributed. Since it is determined whether the attention line La is an edge line based on the continuity c of the image, the boundary between the high luminance area and the low luminance area is detected as an edge line, and an edge in line with a natural human sense Detection can be performed. This effect will be described in detail. FIG. 20 is a diagram illustrating an example of an image for explaining the processing of the edge line detection unit 37. 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, the edge line detection unit 37 determines the part 103 as an edge line only when the luminance difference attribute has continuity in addition to the luminance difference in the part 103. 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 sensation can be performed.

  Returning to FIG. 16, the three-dimensional object detection unit 33 a detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 37. As described above, the three-dimensional object detection device 1a 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 33a detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 37. Specifically, the three-dimensional object detection unit 33a determines whether the amount of edge lines detected by the edge line detection unit 37 is equal to or greater than a predetermined threshold value β, and the amount of edge lines is determined to be a predetermined threshold value β. In the case described above, the edge line detected by the edge line detection unit 37 is determined to be an edge line of a three-dimensional object, thereby detecting the three-dimensional object based on the edge line as the adjacent vehicle V2.

  As described above, the edge line is an aspect of pixel distribution information indicating a predetermined luminance difference, and the “pixel distribution information” in the present embodiment is a three-dimensional object when a captured image is converted into a bird's-eye view image. It can be positioned as information indicating the distribution state of “pixels having a luminance difference equal to or greater than a predetermined threshold” detected along the falling direction. In other words, the three-dimensional object detection unit 33a distributes pixels on the bird's-eye view image obtained by the viewpoint conversion unit 31 in which the luminance difference is equal to or greater than the predetermined threshold t in the direction in which the three-dimensional object falls when the viewpoint is converted into the bird's-eye view image. By detecting the information (edge line), the solid object is detected and the relative value of the solid object is detected based on the detected pixel distribution information.

  In the second embodiment, the threshold value β for detecting the adjacent vehicle is set by the detection reference setting unit 34a. That is, in the second embodiment, the detection reference setting unit 34a detects the brightness of the bird's-eye view image corresponding to the detection areas A1 and A2, and the noise caused by the dark current characteristics of the camera 10 in the detection areas A1 and A2. An area having a luminance value included in a luminance value band in which the occurrence of the error is likely to occur is specified as the specific luminance area. Then, as in the first embodiment, the detection reference setting unit 34a detects the darkness of the bird's-eye view image corresponding to the specific luminance area, and the darkness of the bird's-eye view image corresponding to the detection areas A1 and A2 is detected. The larger the value is (the darker it is), the higher the threshold value β in the specific luminance region is set.

  Further, prior to detecting the three-dimensional object, the three-dimensional object detection unit 33a determines whether or not the edge line detected by the edge line detection unit 37 is correct. The three-dimensional object detection unit 33a determines whether or not the luminance change along the edge line of the bird's-eye view image on the edge line is equal to or greater than a predetermined threshold value tb. When the brightness change of the bird's-eye view image on the edge line is equal to or greater than the threshold value tb, it is determined that the edge line has been detected by erroneous determination. On the other hand, when the luminance change of the bird's-eye view image on the edge line is less than the threshold value tb, it is determined that the edge line is correct. The threshold value tb is a value set in advance by experiments or the like.

  FIG. 21 is a diagram illustrating the luminance distribution of the edge line. FIG. 21A illustrates the edge line and the luminance distribution when the adjacent vehicle V2 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. 21A, it is assumed that the attention line La set in the tire rubber portion of the adjacent vehicle V2 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 adjacent vehicle V2 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. 21B, 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 33a determines whether or not the edge line is detected by erroneous determination. When the luminance change along the edge line is equal to or greater than the predetermined threshold value tb, the three-dimensional object detection unit 33a has detected the edge line by erroneous determination, and the edge line is caused by the three-dimensional object. Judge that it is not. 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. On the other hand, when the change in luminance along the edge line is less than the predetermined threshold value tb, the three-dimensional object detection unit 33a determines that the edge line is an edge line of the three-dimensional object, and the three-dimensional object exists. Judge.

Specifically, the three-dimensional object detection unit 33a calculates the luminance change of the edge line according to 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). . Also, the following Equation 5 represents the luminance distribution according to the sum of 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 evaluate.
[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 33a sums the attributes b for all the attention points Pa on the attention line La and obtains an evaluation value in the vertical equivalent direction, whereby the edge line is caused by the three-dimensional object. It is determined whether or not a three-dimensional object exists.

  As in the first embodiment, the detected object control unit 35 determines whether or not the host vehicle has passed the three-dimensional object based on the temporal change in the relative position of the three-dimensional object with respect to the host vehicle detected by the three-dimensional object detection unit 33a. When it is determined that the vehicle has overtaken the three-dimensional object, the three-dimensional object is detected in the detection target area excluding the shift area behind the traveling direction in the detection areas A1 and A2, as shown in FIG. 13C. The three-dimensional object detection unit 33a is controlled.

  Next, an adjacent vehicle detection method according to the second embodiment will be described. FIG. 22 is a flowchart showing details of the adjacent vehicle detection method according to this embodiment. In FIG. 22, for the sake of convenience, the process for the detection area A1 will be described, but the same process is executed for the detection area A2.

  First, in step S301, the camera 10 captures an image of a predetermined area specified by the angle of view a and the attachment position, and the computer 30a acquires image data of a captured image captured by the camera 10. Next, in step S302, the viewpoint conversion unit 31 performs viewpoint conversion on the acquired image data to generate bird's-eye view image data.

  Next, in step S303, the luminance difference calculator 36 sets the attention line La and the reference line Lr on the detection area A1. At this time, the luminance difference calculation unit 36 sets a line corresponding to a line extending in the vertical direction in the real space as the attention line La, corresponds to a line segment extending in the vertical direction in the real space, and the attention line A line separated from La by a predetermined distance in the real space is set as the reference line Lr.

  Next, in step S304, the luminance difference calculation unit 36 sets a plurality of attention points Pa on the attention line La, and makes reference so that the attention point Pa and the reference point Pr have substantially the same height in the real space. A point Pr is set. 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. Note that the luminance difference calculation unit 36 sets a number of attention points Pa that do not cause a problem when an edge is detected by the edge line detection unit 37.

  Next, in step S305, the luminance difference calculation unit 36 calculates the luminance difference between the attention point Pa and the reference point Pr, which have the same height in the real space. Then, the edge line detection unit 37 calculates the attribute s of each attention point Pa in accordance with Equation 1 above. Next, in step S306, the edge line detection unit 37 calculates the continuity c of the attribute s of each attention point Pa according to the above mathematical formula 2. In step S307, the edge line detection unit 37 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 (step S307 = Yes), the edge line detection unit 37 detects the attention line La as an edge line in step S308. Then, the process proceeds to step S309. If it is determined that the normalized value is not larger than the threshold θ (step S307 = No), the edge line detection unit 37 does not detect the attention line La as an edge line, and the process proceeds to step S309.

  In step S309, the computer 30a determines whether or not the processing in steps S303 to S308 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 (step S309 = No), the processing returns to step S303, a new attention line La is set, and the processing up to step S309 is repeated. On the other hand, when it is determined that the above process has been performed for all the attention lines La (step S309 = Yes), the process proceeds to step S310.

  In step S310, the three-dimensional object detection unit 33a calculates a luminance change along the edge line for each edge line detected in step S308. The three-dimensional object detection unit 33a calculates the luminance change of the edge line according to any one of the above formulas 4, 5, and 6. Next, in step S311, the three-dimensional object detection unit 33a excludes edge lines whose luminance change is equal to or greater than a predetermined threshold value tb from among 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 tb is a value set based on a luminance change generated by characters on the road surface, weeds on the road shoulder, or the like, which is obtained in advance through experiments or the like. On the other hand, the three-dimensional object detection unit 33a determines an edge line whose luminance change is less than the predetermined threshold value tb among the edge lines as an edge line of the three-dimensional object, and thereby detects a three-dimensional object existing in the adjacent vehicle. .

  In steps S312 to S314, the threshold value β for determining whether or not the three-dimensional object detected in step S311 is an adjacent vehicle is set by the detection reference setting unit 34a. First, in step S312, the detection reference setting unit 34a sets the threshold value β to a predetermined value that can be determined to be a four-wheeled vehicle that appears in the detection areas A1 and A2 from the number of edge lines, for example. In step S313, as in step S106 of the first embodiment, the detection reference setting unit 34a determines whether or not the specific luminance area exists in the detection area A1, and if the specific luminance area exists. Proceeds to step S314, and sets a threshold value β according to the degree of darkness of the detection area A1. Note that the detection reference setting unit 34a sets the threshold β set in step S312 as to the region other than the specific luminance region in the detection region A1.

  In step S315, the three-dimensional object detection unit 33a determines whether the amount of the edge line is equal to or larger than the threshold value β set in step S312 or step S314. When it is determined that the amount of the edge line is equal to or larger than the threshold value β (step S315 = Yes), the three-dimensional object detection unit 33a determines in step S316 that an adjacent vehicle exists in the detection area A1. On the other hand, when it is determined that the amount of the edge line is not equal to or greater than the threshold value β (step S315 = No), the three-dimensional object detection unit 33a determines in step S317 that there is no adjacent vehicle in the detection area A1. Thereafter, the process shown in FIG.

  In the second embodiment, as in the first embodiment, the detection control process shown in FIG. 15 is performed in parallel with the adjacent vehicle detection process shown in FIG. Therefore, also in the second embodiment, the control by the detection control unit 35 is performed so that when the host vehicle passes an adjacent vehicle, the three-dimensional object detection unit 33 detects the three-dimensional object only in the detection target region.

  As described above, in the second embodiment, when detecting an adjacent vehicle existing in an adjacent lane by converting a captured image into a bird's-eye view image and detecting edge information of a three-dimensional object from the converted bird's-eye view image. Then, it is determined whether or not the own vehicle has passed the adjacent vehicle. When the host vehicle overtakes an adjacent vehicle, as shown in FIG. 13C, a detection target area excluding the shift area behind the traveling direction is set in the detection areas A1 and A2, and three-dimensional object detection is performed. The unit 33 is caused to detect a three-dimensional object in the set detection target region. Thereby, in 2nd Embodiment, in addition to the effect of 1st Embodiment, when detecting the adjacent vehicle which exists in an adjacent lane based on the edge information extracted from the bird's-eye view image, the own vehicle V1 is an adjacent vehicle. When passing over V2, the light emitted from the headlight of the adjacent vehicle V2 is reflected on the road surface in the mask area in the detection area A1, or the shadow of the adjacent vehicle over which the host vehicle has passed is detected area A1. , A2, or when an image of an adjacent vehicle overtaken by the host vehicle is reflected in a puddle in the detection areas A1 and A2, it is effective to erroneously detect these images as a three-dimensional object. Can be prevented.

  The embodiment described above is described for facilitating the understanding of the present invention, and is not described for limiting the present invention. Therefore, each element disclosed in the above embodiment is intended to include all design changes and equivalents belonging to the technical scope of the present invention.

For example, in the above-described embodiment, a configuration in which the three-dimensional object is detected only in the detection target region by generating the differential waveform DW _t from only the difference image PD _t corresponding to the detection target region in the detection regions A1 and A2. Although illustrated, it is not limited to this configuration, for example, the detection of the three-dimensional object is performed based on only the differential waveform DW _t corresponding to the detection target region among the differential waveforms DW _t generated from the entire detection regions A1 and A2. Thus, the configuration may be such that the detection of the three-dimensional object is performed only in the detection target region. Alternatively, the peak position of the differential waveform DW _t corresponding to the detection target area, only when the peak of the differential waveform DW _t exceeds the threshold value alpha, by determining the adjacent adjacent lane vehicle is present, the three-dimensional object It is good also as a structure which performs a detection only in a detection object area | region.

In the above-described embodiment, the specific luminance region is specified based on the luminance of the difference image PD _t . However, the configuration is not limited to this configuration. For example, the specific luminance region is specified based on the luminance of the captured image. It is good also as composition to do.

  Furthermore, in the above-described embodiment, whether or not the weather is rainy by communicating with an external server (not shown) installed outside the host vehicle and receiving current weather information around the host vehicle from the external server. However, the present invention is not limited to this configuration. For example, it may be configured to determine whether the weather is rainy based on raindrop sensor or wiper operation information.

Further, in the above-described embodiment, when the threshold value α is set according to the darkness degree of the difference image PD _t , the extraction reference value exceeds the extraction reference value while changing the extraction reference value ts to a small value as shown in FIG. An edge component is extracted, the largest extraction reference value ts ′ is specified among the extraction reference values that can extract edge components of a predetermined amount or more, and the darkness level of the difference image PD _t is determined from the size of the extraction reference value ts ′. Although the configuration to detect is illustrated, it is not limited to this configuration. For example, the extraction reference value is set in advance to a small value (for example, a value smaller than ts ′ shown in FIG. 12), and edge components exceeding the extraction reference value are extracted while changing the set extraction reference value to a large value. Then, by specifying the smallest extraction reference value ts ′ among the extraction reference values from which edge components of a predetermined amount or more can be extracted, the degree of darkness of the difference image PD _t is detected from the size of this extraction reference value ts ′. It is good also as composition to do. Further, the extraction reference value ts shown in FIG. 12 is not particularly limited, and may be a value generally used for extracting an edge component from a captured image when traveling at night, for example.

  In the above-described embodiment, when the host vehicle overtakes an adjacent vehicle, as shown in FIG. 13C, the region behind the detection direction A1, A2 is set as a mask region, and the mask Although the configuration in which the three-dimensional object is detected in the detection target region excluding the region is illustrated, the present invention is not limited to this configuration. For example, among the regions behind the detection direction A1 and A2 in the advancing direction, Only a luminance area may be set as a mask area, and a three-dimensional object may be detected in a detection target area excluding the mask area. In this case as well, it is possible to eliminate the influence of the light emitted from the headlights of the adjacent vehicle over which the host vehicle overtakes, and in the regions that are not high in luminance in the regions behind the detection regions A1 and A2, Adjacent vehicles can be detected appropriately in this area. Alternatively, in the same manner as in the above-described embodiment, in addition to setting the region behind detection direction A1 and A2 in the traveling direction as a mask region, the predetermined luminance of the detection region A1 and A2 among the regions ahead of the traveling direction is set. It is also possible to set a high-intensity area equal to or greater than the value as a mask area and detect a three-dimensional object in a detection target area excluding the mask area. Also in this case, in the area behind the detection direction A1, A2, the influence of the light emitted from the headlight of the adjacent vehicle over which the host vehicle overtakes can be eliminated, and the irradiation from the headlight of the adjacent vehicle can be eliminated. Even when the emitted light is irradiated to the area ahead of the traveling direction in the detection areas A1 and A2, the influence of the light irradiated from the headlight of the adjacent vehicle can be eliminated.

  Furthermore, in the above-described embodiment, when the host vehicle overtakes an adjacent vehicle, as shown in FIG. 13C, a region behind the detection direction is set as a mask region, and the mask region is excluded. However, the present invention is not limited to this configuration. For example, when the host vehicle passes an adjacent vehicle, the detection region may be narrowed forward. Also in this case, it is possible to eliminate the influence of the light emitted from the headlight of the adjacent vehicle over which the host vehicle has overtaken.

  The camera 10 of the above-described embodiment corresponds to the imaging unit of the present invention, the viewpoint conversion unit 31 corresponds to the image conversion unit of the present invention, and the alignment unit 32 and the three-dimensional object detection units 33 and 33a of the present invention. The three-dimensional object detection unit 33, 33a, the detection reference setting unit 34, 34a, and the detection control unit 35 correspond to the control unit of the present invention.

DESCRIPTION OF SYMBOLS 1, 1a ... Three-dimensional object detection apparatus 10 ... Camera 20 ... Vehicle speed sensor 30, 30a ... Computer 31 ... Viewpoint conversion part 32 ... Position alignment part 33, 33a ... Three-dimensional object detection part 34, 34a ... Detection reference setting part 35 ... Detection control Unit 36 ... luminance difference calculation unit 37 ... edge line detection unit 40 ... communication device a ... angle of view A1, A2 ... detection area CP ... intersection DP ... difference pixel DW _t , DW _t '... difference waveform DW _{t1 to} DW _m , DW _{m + k to} DW _tn ... small areas L1, L2 ... ground lines La and Lb ... line PB _{t in the} direction in which the three-dimensional object collapses ... bird's-eye view image PD _t ... difference image V1 ... own vehicle V2 ... adjacent vehicle

Claims (15)

Imaging means for imaging the rear of the vehicle;
Image conversion means for converting the captured image obtained by the imaging means into a bird's eye view image;
The positions of the bird's-eye view images at different times obtained by the image conversion means are aligned in bird's-eye view, and the number of pixels indicating a predetermined difference is counted on the aligned difference image to form a frequency distribution. The difference waveform information is generated in the step, and a three-dimensional object existing in a predetermined detection area set behind the host vehicle is detected based on the difference waveform information, and the three-dimensional object with respect to the host vehicle is detected based on the difference waveform information. A three-dimensional object detection means capable of detecting a relative position;
Based on the time change of the relative position of the three-dimensional object, it is determined whether or not the own vehicle has overtaken the three-dimensional object. And a control means for causing the three-dimensional object detection means to detect the three-dimensional object in a detection target area excluding the mask area in the detection area. Solid object detection device. Imaging means for imaging the rear of the vehicle;
Image conversion means for converting the captured image obtained by the imaging means into a bird's eye view image;
Edge information is detected from the bird's eye view image obtained by the image conversion means, and based on the edge information, a solid object existing in a predetermined detection area set behind the host vehicle is detected, and the edge information is included in the edge information. A three-dimensional object detection means for detecting a relative position of the three-dimensional object with respect to the host vehicle,
Based on the time change of the relative position of the three-dimensional object, it is determined whether or not the own vehicle has overtaken the three-dimensional object. And a control means for causing the three-dimensional object detection means to detect the three-dimensional object in a detection target area excluding the mask area in the detection area. Solid object detection device. The three-dimensional object detection device according to claim 1 or 2,
The control means calculates a relative movement speed of the host vehicle with respect to the three-dimensional object based on a temporal change in the relative position of the three-dimensional object, and when the calculated relative movement speed of the host vehicle is equal to or higher than a predetermined speed, A three-dimensional object detection device, characterized in that it is determined that a vehicle has overtaken the three-dimensional object. The three-dimensional object detection device according to claim 1 or 2,
The control means moves from the front to the back in the traveling direction in the detection area from the time of the relative position of the three-dimensional object relative to the host vehicle in a predetermined time, and from the imaging means to the three-dimensional object. A three-dimensional object detection device that determines that the own vehicle has overtaken the three-dimensional object when it can be determined that the rear distance of the vehicle exceeds a predetermined distance. The three-dimensional object detection device according to any one of claims 1 to 4,
The control means calculates a relative movement speed of the host vehicle with respect to the three-dimensional object based on a temporal change in the relative position of the three-dimensional object, and the higher the calculated relative movement speed of the host vehicle, the narrower the mask area. A three-dimensional object detection device characterized by setting. The three-dimensional object detection device according to any one of claims 1 to 5,
The control means acquires the current weather information, and sets the mask area wider when it is determined that it is raining based on the acquired weather information than when it is determined that it is not raining The three-dimensional object detection apparatus characterized by the above-mentioned. The three-dimensional object detection device according to any one of claims 1 to 6,
When the control unit determines that the vehicle has overtaken the three-dimensional object, another three-dimensional object that is different from the three-dimensional object subjected to the overtaking determination by the three-dimensional object detection unit is the target of the overtaking determination. The three-dimensional object detection apparatus, wherein the mask area is set narrower when the three-dimensional object is detected ahead of the traveling direction. The three-dimensional object detection device according to any one of claims 1 to 7,
The three-dimensional object detection device, wherein the control means sets the mask area for a predetermined time when it is determined that the host vehicle has overtaken the three-dimensional object. The three-dimensional object detection device according to claim 8,
The control means calculates a relative movement speed of the host vehicle with respect to the three-dimensional object based on a temporal change in the relative position of the three-dimensional object, and shortens the predetermined time as the calculated relative movement speed of the host vehicle increases. A three-dimensional object detection device characterized by setting. The three-dimensional object detection device according to claim 8 or 9,
The said control means acquires the present weather information, and when it is judged that it is raining based on the acquired weather information, compared with the case where it is judged that it is not raining, the said predetermined time is set longer The three-dimensional object detection apparatus characterized by the above-mentioned. The three-dimensional object detection device according to any one of claims 8 to 10,
When the control unit determines that the vehicle has overtaken the three-dimensional object, another three-dimensional object that is different from the three-dimensional object subjected to the overtaking determination by the three-dimensional object detection unit is the target of the overtaking determination. The three-dimensional object detection apparatus, wherein the predetermined time is set shorter when the three-dimensional object is detected in front of the traveling direction. The three-dimensional object detection device according to any one of claims 3 to 11 dependent on claim 1 or claim 1,
The three-dimensional object detection unit generates the difference waveform information by counting the number of pixels indicating a difference equal to or greater than a predetermined first threshold on the difference image and performing frequency distribution, and the difference waveform information is determined to be a predetermined first value. When the threshold is 2 or more, the solid object is detected based on the difference waveform information,
The control means specifies a region having a luminance value included in a luminance value band in which disturbance due to dark current characteristics of the imaging unit is likely to occur among the detection target regions excluding the mask region from the detection region. The specific luminance region in the detection target region is identified as the region, the darkness level of the image corresponding to the specific luminance region is detected, and the darkness level of the image corresponding to the detected specific luminance region is higher The three-dimensional object detection apparatus is characterized in that the first threshold value or the second threshold value is set high so that the three-dimensional object is difficult to detect. The three-dimensional object detection device according to any one of claims 3 to 11 dependent on claim 2 or claim 2,
The three-dimensional object detection means detects edge information not less than a predetermined first threshold from the bird's-eye view image, and if the edge information is not less than a predetermined second threshold, the three-dimensional object is detected based on the edge information. Detect
The control means specifies a region having a luminance value included in a luminance value band in which disturbance due to dark current characteristics of the imaging unit is likely to occur among the detection target regions excluding the mask region from the detection region. The specific luminance region in the detection target region is identified as the region, the darkness level of the image corresponding to the specific luminance region is detected, and the darkness level of the image corresponding to the detected specific luminance region is higher The three-dimensional object detection apparatus is characterized in that the first threshold value or the second threshold value is set high so that the three-dimensional object is difficult to detect. The three-dimensional object detection device according to claim 12 or 13,
When detecting the degree of darkness of the image corresponding to the specific luminance region, the control means changes an edge component exceeding the extraction criterion from the image based on the captured image while changing the size of a predetermined extraction criterion. By extracting, the value of the extraction criterion that can extract the edge component of a predetermined amount or more is specified, and the darkness level of the image corresponding to the specific luminance region increases as the specified extraction criterion value increases. A three-dimensional object detection device characterized in that it is detected with Imaging means for imaging the rear of the vehicle;
Image conversion means for converting the captured image obtained by the imaging means into a bird's eye view image;
On the bird's-eye view image obtained by the image conversion means, in the direction in which the three-dimensional object collapses when the viewpoint is converted into the bird's-eye view image, detecting distribution information of pixels having a luminance difference of a predetermined threshold or more, Based on the distribution information of the pixels, the solid object existing in a predetermined detection area set behind the host vehicle is detected, and the relative position of the three-dimensional object with respect to the host vehicle is determined based on the distribution information of the pixels. Three-dimensional object detection means for detecting
Based on the time change of the relative position of the three-dimensional object, it is determined whether or not the own vehicle has overtaken the three-dimensional object. And a control means for causing the three-dimensional object detection means to detect the three-dimensional object in a detection target area excluding the mask area in the detection area. Solid object detection device.

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