JP6011110B2 - Three-dimensional object detection apparatus and three-dimensional object detection method - Google Patents

Description

  The present invention relates to a three-dimensional object detection device and a three-dimensional object detection method.

  2. Description of the Related Art Conventionally, there is known a technique for converting two captured images captured at different times into a bird's-eye view image and detecting a three-dimensional object based on the difference between the two converted bird's-eye view images (Patent Literature). 1).

JP 2008-227646 A

  When detecting a three-dimensional object existing in an adjacent lane as an adjacent vehicle based on a captured image captured by a camera at night, it is difficult to detect a three-dimensional object such as an adjacent vehicle because the surrounding luminance is low. was there. In particular, when foreign objects such as raindrops are attached to the lens of the camera, the image of the three-dimensional object is distorted or blurred by the foreign object such as raindrops, even though there is no adjacent vehicle. In some cases, the captured three-dimensional object is erroneously detected as an adjacent vehicle.

  The problem to be solved by the present invention is to provide a three-dimensional object detection device capable of appropriately detecting an adjacent vehicle even when foreign matter such as raindrops adheres to a lens of a camera during night driving.

  The present invention generates a differential waveform information based on a differential image of a bird's eye view image, detects a solid object based on the differential waveform information, and determines whether the detected three-dimensional object is an adjacent vehicle. In the detection device, as the difference between the first moving speed of the three-dimensional object calculated based on the difference waveform information and the second moving speed of the three-dimensional object calculated based on the light source caused by the three-dimensional object is larger, The above-described problem is solved by suppressing the determination that the three-dimensional object is an adjacent vehicle.

  According to the present invention, the greater the difference between the first moving speed of the three-dimensional object calculated based on the difference waveform information and the second moving speed of the three-dimensional object calculated based on the light source caused by the three-dimensional object. , Foreign matter such as raindrops is attached to the lens, and it is determined that there is a high possibility of erroneous detection of an adjacent vehicle, and it is prevented from being determined that a three-dimensional object is an adjacent vehicle, thereby attaching to the lens It is possible to effectively prevent the detected foreign matter or the three-dimensional object other than the adjacent vehicle from being erroneously detected as the adjacent vehicle.

It is a schematic block diagram of the vehicle carrying a solid-object detection apparatus. It is a top view which shows the driving state of the vehicle of FIG. It is a block diagram which shows the detail of a computer. It is a figure for demonstrating the outline | summary of the process of a position alignment part, (a) is a top view which shows the movement state of a vehicle, (b) is an image which shows the outline | summary of position alignment. It is the schematic which shows the mode of the production | generation of the difference waveform by a solid-object detection part. It is a figure which shows an example of the threshold value for detecting a difference waveform and a solid object. It is a figure which shows the small area | region divided | segmented by the solid-object detection part. It is a figure which shows an example of the histogram obtained by a solid-object detection part. It is a figure which shows the weighting by a solid-object detection part. It is a figure which shows the other example of the histogram obtained by a solid-object detection part. It is a figure which shows an example of the captured image imaged when raindrops have adhered to the lens of the camera. (A) is a figure which shows an example of the differential waveform produced | generated in the scene where the adjacent vehicle exists in a detection area, but the raindrop does not adhere to the position corresponding to this detection area, (B) FIG. 6B is a diagram illustrating an example of a differential waveform generated in a scene in which no adjacent vehicle exists in the detection area but raindrops are attached to a position corresponding to the detection area, and FIG. It is a figure which shows an example of the difference waveform produced | generated in the scene where the adjacent vehicle exists in an area | region, and the raindrop has adhered to the position corresponding to this detection area. (A) is a figure which shows an example of the difference vehicle speed when the foreign material has not adhered to the lens, and the differential vehicle speed when the foreign material has adhered to the lens, and (B) is that the foreign material has not adhered to the lens. It is a figure which shows an example of the headlight vehicle speed in case the foreign substance has adhered to the headlight vehicle speed in the case, and a lens. (A) is a figure which shows an example of the difference vehicle speed and headlight vehicle speed when the foreign material has not adhered to the lens, (B) is the difference vehicle speed and headlight vehicle speed when the foreign material has adhered to the lens. It is a figure which shows an example. It is a figure which shows an example of the relationship between the average value of a speed difference evaluation value, and a lens state determination value. It is a figure which shows an example of the relationship between a lens state determination value and a difference threshold value. It is a flowchart which shows the adjacent vehicle detection process which concerns on 1st Embodiment. It is a flowchart which shows the threshold value change process which concerns on 1st Embodiment. It is a block diagram which shows the detail of the computer which concerns on 2nd Embodiment. It is a figure which shows the driving | running | working state of a vehicle, (a) is a top view which shows positional relationships, such as a detection area, (b) is a perspective view which shows positional relationships, such as a detection area in real space. It is a figure for demonstrating operation | movement of the brightness | luminance difference calculation part which concerns on 2nd Embodiment, (a) is a figure which shows the positional relationship of the attention line, reference line, attention point, and reference point in a bird's-eye view image, (b). FIG. 4 is a diagram illustrating a positional relationship among attention lines, reference lines, attention points, and reference points in real space. It is a figure for demonstrating the detailed operation | movement of the brightness | luminance difference calculation part which concerns on 2nd Embodiment, (a) is a figure which shows the detection area | region in a bird's-eye view image, (b) is the attention line and reference line in a bird's-eye view image. It is a figure which shows the positional relationship of an attention point and a reference point. It is a figure which shows the example of an image for demonstrating edge detection operation | movement. It is a figure which shows the luminance distribution on an edge line and an edge line, (a) is a figure which shows luminance distribution in case a solid object (adjacent vehicle) exists in a detection area, (b) is a figure which shows a solid object in a detection area. It is a figure which shows the luminance distribution when it does not exist. It is a flowchart which shows the adjacent vehicle detection method which concerns on 2nd Embodiment. It is a flowchart which shows the threshold value change process which concerns on 2nd Embodiment. It is a block diagram which shows the detail of the computer which concerns on 3rd Embodiment. It is FIG. (1) for demonstrating the detection method of the foreign material by a foreign material detection part. It is FIG. (2) for demonstrating the detection method of the foreign material by a foreign material detection part.

<< First Embodiment >>
FIG. 1 is a schematic configuration diagram of a vehicle equipped with a three-dimensional object detection device 1 according to the present embodiment. The three-dimensional object detection device 1 according to the present embodiment is intended to detect another vehicle (hereinafter also referred to as an adjacent vehicle V2) existing in an adjacent lane that may be contacted when the host vehicle V1 changes lanes. To do. As shown in FIG. 1, the three-dimensional object detection device 1 according to the present embodiment includes a camera 10, a vehicle speed sensor 20, and a calculator 30.

  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.

  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 and the vehicle speed sensor 20 are also illustrated in order to clarify the connection relationship.

  As shown in FIG. 3, the computer 30 includes a viewpoint conversion unit 31, an alignment unit 32, a difference waveform generation unit 33, a three-dimensional object detection unit 34, a difference vehicle speed calculation unit 35, and a headlight detection unit 36. The headlight vehicle speed calculation unit 37, the speed difference calculation unit 38, and the threshold value change unit 39 are provided. 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 the present embodiment, the alignment unit 32 aligns the positions of the bird's-eye view images at different times on the bird's-eye view, and obtains the aligned bird's-eye view image. This can be performed with accuracy according to the type of detection target and the required detection accuracy. For example, it may be a strict alignment process that aligns positions based on the same time and the same position, or may be a loose alignment process that grasps the coordinates of each bird's-eye view image.

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. When the difference value is equal to or greater than the threshold value th, the pixel value of the difference image PD _t is set to “1”, and when the absolute value is less than the predetermined difference threshold value th, the pixel value of the difference image PD _t is set to “0”. Thus, data of the difference image PD _t as shown on the right side of FIG. 4B can be generated. In the present embodiment, the difference threshold th is changed by a threshold changing unit 39 described later, and when the difference threshold th is changed by the threshold changing unit 39, the difference threshold th changed by the threshold changing unit 39 is used. Thus, the pixel value of the difference image PD _t is detected.

Then, the difference waveform generation unit 33 generates a difference waveform based on the data of the difference image PD _t shown in FIG. Specifically, the differential waveform generation unit 33 generates a differential waveform in a detection region set to the left and right rear of the host vehicle V1.

  Here, the three-dimensional object detection device 1 of the present example is intended to calculate the movement distance for the adjacent vehicle V2 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 and A2 are set on the left and right rear sides 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.

  In this example, as shown in FIG. 2, the sides (sides along the traveling direction) of the set detection areas A1 and A2 on the own vehicle V1 side are recognized 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 differential waveform generator 33 generates a differential waveform. As illustrated in FIG. 5, the differential waveform generation unit 33 calculates a differential waveform from a portion corresponding to the detection areas A1 and A2 in the differential image PD _t (right diagram in FIG. 4B) calculated by the alignment unit 32. DW _t is generated. At this time, the differential waveform generation unit 33 generates the differential waveform DW _t along the direction in which the three-dimensional object falls due to 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, the differential waveform generation unit 33 defines a line La in a direction in which the three-dimensional object falls on the data of the differential image PD _t . Then, the difference waveform generation 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 differential waveform generation unit 33 counts the number of differential pixels DP, and then obtains an intersection CP between the line La and the ground line L1. Then, the differential waveform generation unit 33 associates the intersection CP with the count number, determines the horizontal axis position based on the position of the intersection CP, that is, the position on the vertical axis in the right diagram of FIG. The axis position, that is, the position on the right and left axis in the right diagram of FIG.

Similarly, the differential waveform generation unit 33 defines lines Lb, Lc... In the direction in which the three-dimensional object falls, counts the number of differential 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 differential waveform generation 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.

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 determining the vertical axis position from the count number of the difference pixel DP, the difference waveform generation unit 33 is normalized based on the distances where 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. Therefore, in determining the vertical axis position from the count number in FIG. 5, the differential waveform generation 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.

The three-dimensional object detection unit 34 detects a three-dimensional object existing in the detection areas A1 and A2 based on the difference waveform DW _t generated by the difference waveform generation unit 33. Here, FIG. 6 is a diagram for explaining a method of detecting a three-dimensional object by the three-dimensional object detection unit 34, and illustrates an example of the difference waveform DW _t and a threshold value α for detecting the three-dimensional object. As illustrated in FIG. 6, the three-dimensional object detection unit 34 determines whether or not the peak of the generated differential waveform DW _t is _equal to or greater than a predetermined threshold value α corresponding to the peak position of the differential waveform DW _t. Then, it is determined whether or not a three-dimensional object exists in the detection areas A1 and A2. Then, when the peak of the difference waveform DW _t is less than the predetermined threshold value α, the three-dimensional object detection unit 34 determines that there is no three-dimensional object in the detection areas A1 and A2, while the peak of the difference waveform DW _t Is greater than or equal to a predetermined threshold value α, it is determined that a three-dimensional object exists in the detection areas A1 and A2. Further, in the present embodiment, the three-dimensional object detection unit 34 is the adjacent vehicle V2 in which the detected three-dimensional object exists in the adjacent lane based on the moving speed of the three-dimensional object detected by the differential vehicle speed calculation unit 35 described later. Determine whether or not.

Difference speed calculation unit 35, in contrast with the differential waveform DW _t-1 of the previous differential waveform DW _t and a time instant at the current time, the difference speed (relative speed of the adjacent vehicle V2 with respect to the vehicle V1) moving speed of the three-dimensional object Calculate as That is, the difference vehicle speed calculation unit 35 calculates the relative vehicle speed of the adjacent vehicle V2 as the difference vehicle speed from the time change of the difference waveforms DW _t and DW _t−1 .

More specifically, the differential vehicle speed calculation unit 35 divides the differential waveform DW _t into a plurality of small areas DW _{t1 to} DW _tn (n is an arbitrary integer equal to or greater than 2) as shown in FIG. FIG. 7 is a diagram illustrating the small areas DW _{t1 to} DW _tn divided by the differential vehicle speed calculation unit 35. The small areas 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 differential vehicle speed calculation unit 35 obtains an offset amount (a movement amount of the differential waveform in the horizontal axis direction (vertical direction in FIG. 7)) for each of the small areas 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). In this case, the difference speed calculation unit 35, 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 difference vehicle speed calculation unit 35 counts the offset amount obtained for each of the small areas DW _{t1 to} DW _tn and forms a histogram.

FIG. 8 is a diagram illustrating an example of a histogram obtained by the differential vehicle speed calculation unit 35. As shown in FIG. 8, there is some variation in 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. For this reason, the differential vehicle speed calculation unit 35 forms a histogram of the offset amount including variations, and calculates the movement distance from the histogram. At this time, the differential vehicle speed calculation unit 35 calculates the moving distance of the three-dimensional object (adjacent vehicle V2) from the maximum value of the histogram. That is, in the example shown in FIG. 8, the difference vehicle speed calculation unit 35 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 three-dimensional object (adjacent vehicle V2) with respect to the own vehicle. For this reason, when calculating the absolute movement distance, the difference vehicle speed calculation unit 35 calculates the absolute movement distance based on the obtained movement distance τ ^* and the signal from the vehicle speed sensor 20.

As described above, in the present embodiment, by calculating the moving distance of the three-dimensional object (adjacent vehicle V2) 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. Therefore, the movement distance is calculated from the offset amount of the one-dimensional information called the waveform, and the calculation cost can be suppressed in calculating the movement distance. 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 difference speed calculation unit 35 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. 9 is a diagram illustrating weighting by the differential vehicle speed calculation unit 35.

As shown in FIG. 9, the small area 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. The difference vehicle speed calculation unit 35 reduces the weight for such a 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. The differential vehicle speed calculation unit 35 increases the weight for such a 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 difference vehicle speed calculation unit 35 calculates the movement distance from the offset amount of the difference waveform DW _t when the error between the difference waveform DW _t and the difference 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 differential vehicle speed calculation unit 35 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 differential vehicle speed calculation unit 35 ignores the offset amount corresponding to the stationary object among the maximum values of the histogram and calculates the moving distance of the three-dimensional object.

  FIG. 10 is a diagram illustrating another example of a histogram obtained by the differential vehicle speed calculation unit 35. When a stationary object is present in addition to a three-dimensional object 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 differential vehicle speed calculation unit 35 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, if there are a plurality of maximum values, it is assumed that there are a plurality of three-dimensional objects within the angle of view of the camera 10. However, it is extremely rare that a plurality of three-dimensional objects exist in the detection areas A1 and A2. For this reason, the differential vehicle speed calculation unit 35 stops the calculation of 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.

  The headlight detector 36 detects the headlight of the adjacent vehicle V2 that travels behind the host vehicle V1 based on the captured image captured by the camera 10. Specifically, the headlight detection unit 36 detects the light source that can be determined as the headlight of the adjacent vehicle V2 based on the captured image, thereby detecting the light source as the headlight of the adjacent vehicle V2. In the present embodiment, the headlight detection unit 36 detects an image area whose brightness difference from the surrounding area is greater than or equal to a predetermined value and a size greater than or equal to a predetermined value as a headlight candidate area of the adjacent vehicle V2. To do. Further, the headlight detection unit 36 distinguishes between the headlight of the adjacent vehicle V2 and a light source such as a streetlight in the vehicle width direction from the host vehicle V1 to the candidate area, and the rear from the camera 10 to the candidate area. Based on the distance or the like, the headlight of the adjacent vehicle V2 is detected by specifying an image region corresponding to the headlight of the adjacent vehicle V2 from among the headlight candidate regions of the plurality of adjacent vehicles V2.

  The headlight vehicle speed calculation unit 37 calculates the relative vehicle speed of the adjacent vehicle V2 with respect to the host vehicle V1 as the headlight vehicle speed based on the headlight of the adjacent vehicle V2 detected by the headlight detection unit 36. Specifically, the headlight vehicle speed calculation unit 37 calculates the movement distance of the headlight in a predetermined time based on the change in the position of the headlight of the adjacent vehicle V2 detected at different times, and calculates the calculated headlight By differentiating the moving distance with respect to time, the relative vehicle speed of the headlight of the adjacent vehicle V2 is calculated as the headlight vehicle speed.

The speed difference calculation unit 38 calculates the difference between the headlight vehicle speed VELhl calculated by the headlight vehicle speed calculation unit 37 and the difference vehicle speed VELsa calculated by the difference vehicle speed calculation unit 35 as shown in Equation 1 below. Normalization is performed with the vehicle speed VELv1 of the vehicle V1, and the difference between the normalized headlight vehicle speed VELhl and the differential vehicle speed VELsa is calculated as a speed difference evaluation value VELdif.

Further, the speed difference calculation unit 38 repeatedly calculates the speed difference evaluation value VELdif based on the above formula 1 every time the headlight of the adjacent vehicle V2 is detected, and for a predetermined number (for example, 10). Based on the speed difference evaluation value VELdif calculated for the adjacent vehicle V2, an average value VELdif _ave of the speed difference evaluation values is calculated.

The threshold value changing unit 39 sets the average value VELdif _ave of the speed difference evaluation value in order to prevent foreign matters such as raindrops attached to the lens and a three-dimensional object other than the adjacent vehicle V2 from being erroneously detected as the adjacent vehicle V2. Based on this, the degree of foreign matter such as raindrops adhering to the lens is calculated as a lens state determination value, and the difference threshold th for detecting a three-dimensional object from the difference image is changed according to the calculated lens state determination value To do.

Here, FIG. 11 is a diagram illustrating an example of a captured image captured in a scene where raindrops are attached to the lens. FIG. 12A shows an example of a differential waveform DW _t generated in a scene where the adjacent vehicle V2 exists in the detection area but no raindrops are attached to the position corresponding to the detection area. FIG. 12B is an example of the differential waveform DW _t generated in a scene in which the adjacent vehicle V2 does not exist in the detection area, but raindrops are attached to the position corresponding to the detection area. FIG. Further, FIG. 12C shows a scene in which the adjacent vehicle V2 exists in the detection area and raindrops are attached to the position corresponding to the detection area, like the detection area A1 shown in FIG. in is a diagram showing an example of a generated difference waveform DW _t.

For example, in the scene where the adjacent vehicle V2 exists in the detection area but no raindrop is attached to the position corresponding to the detection area, as shown in FIG. 12A, the difference caused by the adjacent vehicle V2 Waveform DW _t is generated. On the other hand, even when the adjacent vehicle V2 does not exist in the detection area, if raindrops are attached to the position corresponding to the detection area, as shown in FIG. The resulting differential waveform DW _t is generated.

Therefore, when the adjacent vehicle V2 exists in the detection area and raindrops are attached to the position corresponding to the detection area as in the detection area A1 shown in FIG. as shown, the differences due to foreign matter such as raindrops on the lens will be included in the differential waveform DW _t, it may not be adequately generate a difference waveform DW _t due to the adjacent vehicle V2. Further, when the adjacent vehicle V2 exists in the detection area and raindrops are attached at a position corresponding to the detection area, the image of the adjacent vehicle V2 is caused by foreign matters such as raindrops attached to the lens. In some cases, the image of the adjacent vehicle V2 cannot be appropriately captured due to distortion or blurring. In such a case, as shown in FIG. 12C, the differential waveform DW _t resulting from the adjacent vehicle V2 Is difficult to generate. Thus, when a foreign matter such as raindrops on the lens is attached is not reflected differences due to the change of the image of the adjacent vehicle V2 is the difference waveform DW _t, differential speed calculated based on the differential waveform DW _t And an error may occur in the actual relative vehicle speed of the adjacent vehicle V2.

Here, FIG. 13A is a diagram illustrating an example of the differential vehicle speed calculated when a foreign object adheres to the lens and the differential vehicle speed calculated when a foreign object does not adhere to the lens. . For example, in the case where no foreign matter is attached to the lens, an image of the adjacent vehicle V2 can be appropriately captured. Therefore, a differential waveform DW _t resulting from the adjacent vehicle V2 is generated, and based on the differential waveform DW _t , The relative vehicle speed (difference vehicle speed) of the adjacent vehicle V2 can be calculated appropriately. Therefore, as shown in FIG. 13A, when no foreign matter is attached to the lens, the error between the actual relative vehicle speed and the differential vehicle speed of the adjacent vehicle V2 becomes small. On the other hand, when a foreign object such as a raindrop is attached to the lens, an image of the adjacent vehicle V2 cannot be appropriately captured. Therefore, the differential waveform DW _t resulting from the adjacent vehicle V2 due to the foreign object such as a raindrop. the can not be appropriately generated, as shown in FIG. 13 (a), based on the difference waveform DW _t, it is sometimes impossible to properly calculate the relative speed of the adjacent vehicle V2 (difference speed). For this reason, when a foreign object is attached to the lens, an error between the actual relative vehicle speed of the adjacent vehicle V2 and the difference vehicle speed may increase.

  On the other hand, the light image of the headlight with high brightness is less affected by foreign matter such as raindrops, and even when foreign matter such as raindrops adheres to the lens, the adjacent vehicle is based on the headlight of the adjacent vehicle V2. The relative vehicle speed (headlight vehicle speed) of V2 can be calculated appropriately. Here, FIG. 13B is a diagram showing an example of the headlight vehicle speed calculated when a foreign object adheres to the lens and the headlight vehicle speed calculated when no foreign object adheres to the lens. It is. As shown in FIG. 13B, even when foreign matter such as raindrops is attached to the lens, the headlight vehicle speed calculated based on the headlight of the adjacent vehicle V2 is higher than the difference vehicle speed. The error of V2 with the actual relative vehicle speed becomes small.

That is, when no foreign matter such as raindrops is attached to the lens, the difference between the headlight vehicle speed and the differential vehicle speed is small as shown in FIG. 14A, but foreign matter such as raindrops is attached to the lens. If there is an error, an error may occur in the differential vehicle speed calculated based on the differential waveform DW _t , thereby increasing the difference between the headlight vehicle speed and the differential vehicle speed as shown in FIG. . In the present embodiment, the threshold changing unit 39 determines whether or not a foreign matter such as raindrops is attached to the lens based on the difference between the headlight vehicle speed and the differential vehicle speed. That is, when the difference between the headlight vehicle speed and the differential vehicle speed is large, the threshold value changing unit 39 determines that a foreign matter such as raindrops is attached to the lens, while the difference between the headlight vehicle speed and the differential vehicle speed is determined. Is small, it is determined that no foreign matter such as raindrops is attached to the lens. 14A is a diagram showing an example of the headlight vehicle speed and the differential vehicle speed when no foreign matter such as raindrops is attached to the lens, and FIG. 14B is a diagram showing foreign matter such as raindrops on the lens. It is a figure which shows an example of the headlight vehicle speed and the difference vehicle speed in the case where has adhered.

More specifically, as shown in FIG. 15, the threshold value changing unit 39 calculates an average value VELdif _ave of speed difference evaluation values based on the difference between the headlight vehicle speed and the difference vehicle speed, which is calculated by the speed difference calculating unit 38. The larger the value, the more foreign substances such as raindrops are attached to the lens, and the lens is judged to be dirty, and the lens state determination value indicating the degree to which foreign substances such as raindrops are attached to the lens is calculated higher. FIG. 15 is a diagram illustrating an example of the relationship between the average value VELdif _ave of the speed difference evaluation value and the lens state determination value.

  Then, as shown in FIG. 16, the threshold value changing unit 39 determines that a foreign matter such as raindrops is attached to the lens as the lens state determination value is higher. In order to prevent a three-dimensional object other than the vehicle V2 from being erroneously detected as the adjacent vehicle V2, the difference threshold th for detecting the three-dimensional object (adjacent vehicle V2) is changed to a high value. FIG. 16 is a diagram illustrating an example of the relationship between the lens state determination value and the difference threshold th.

In addition, when calculating the lens state determination value, the threshold value changing unit 39 determines whether or not it is raining. If it is determined that it is raining, as illustrated in FIG. The relationship between the average value VELdif _ave of the speed difference evaluation value and the lens state determination value is changed so that it is easy to determine that the foreign matter is attached. This makes it easier to determine that there are foreign objects such as raindrops on the lens during rainy weather when it is easy for foreign objects such as raindrops to adhere to the lens. Can be determined. Although the method for determining whether or not it is raining is not particularly limited, the threshold value changing unit 39 can determine whether or not it is raining based on, for example, the operation state of a raindrop sensor or a wiper. For example, the threshold value changing unit 39 causes the raindrop sensor to irradiate infrared light toward the lens, and detects the attenuation amount of the irradiated infrared light attenuated by the raindrop, thereby detecting the raindrop amount on the lens surface. As a result of the detection, when the amount of raindrops is a certain amount or more, it can be determined that it is raining. The threshold value changing unit 39 can also determine that it is raining when the wiper operating intensity is equal to or greater than a certain intensity.

  Moreover, the foreign material adhering to the lens of the camera 10 is not limited to raindrops, and includes, for example, scales after the raindrops are dried and muddy water. In other words, the threshold value changing unit 39 determines the degree of contamination of the lens as a lens state determination value in order to prevent erroneous detection of the adjacent vehicle V2 due to such foreign matter even when foreign matter such as dirt or muddy water adheres to the lens. And the difference threshold th can be changed based on the calculated lens state determination value. Note that the threshold value changing unit 39 detects, for example, that when the headlight of the adjacent vehicle V2 cannot be detected for a certain amount of time or more after detecting the headlight of the adjacent vehicle V2, the scale adheres to the lens of the camera 10. Can be determined.

Next, the adjacent vehicle detection process according to the present embodiment will be described. FIG. 17 is a flowchart illustrating the adjacent vehicle detection process according to the first embodiment. As shown in FIG. 17, first, the computer 30 acquires captured image data from the camera 10 (step S101), 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). . Specifically, 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, and when the absolute value is equal to or greater than a predetermined difference threshold th, the difference image PD _t Is set to “1”, and when the absolute value is less than the predetermined difference threshold th, the pixel value of the difference image PD _t is set to “0”. Note that the difference threshold th for calculating the pixel value of the difference image PD _t may be changed in a threshold change process described later in order to suppress detection of a three-dimensional object, and the difference threshold th is changed. The changed difference threshold th is used in step S103. Then, the differential waveform generating 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 three-dimensional object detection unit 34 determines whether or not the peak of the differential waveform DW _t is greater than or _{equal to} a predetermined threshold value α (step S105). 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 S105 = No), the three-dimensional object detection unit 34 determines that there is no three-dimensional object and there is no adjacent vehicle V2 ( Step S114). 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 differential waveform DW _t is equal to or greater than the threshold value α (step S105 = Yes), the three-dimensional object detection unit 34 determines that a three-dimensional object exists in the adjacent lane, and proceeds to step S106. The differential vehicle speed calculation unit 35 divides the differential waveform DW _t into a plurality of small areas DW _{t1 to} DW _tn . Next, the differential vehicle speed calculation unit 35 performs weighting for each of the small areas DW _{t1 to} DW _tn (step S107), calculates an offset amount for each of the small areas DW _{t1 to} DW _tn (step S108), and adds the weight. A histogram is generated (step S109).

  Then, the difference vehicle speed calculation unit 35 calculates a relative movement distance that is a movement distance of the three-dimensional object with respect to the host vehicle V1 based on the histogram (step S110). Next, the differential vehicle speed calculation unit 35 calculates the absolute movement speed of the three-dimensional object from the relative movement distance (step S111). At this time, the differential vehicle speed calculation unit 35 calculates the relative movement speed by differentiating the relative movement distance with respect to time, and calculates the absolute movement speed by adding the own vehicle speed detected by the vehicle speed sensor 20.

  Thereafter, the three-dimensional object detection unit 34 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 V1 is +60 km / h or less (step S112). . When both are satisfied (step S112 = Yes), the three-dimensional object detection unit 34 determines that the detected three-dimensional object is the adjacent vehicle V2 existing in the adjacent lane, and the adjacent vehicle V2 exists in the adjacent lane (step S113). ). Then, the process shown in FIG. 17 ends. On the other hand, when either one is not satisfied (step S112 = No), the three-dimensional object detection unit 34 determines that the adjacent vehicle V2 does not exist in the adjacent lane (step S114). And it returns to step S101 and repeats the process shown in FIG.

  In the present embodiment, the left and right rear sides of the host vehicle V1 are set as detection areas A1 and A2, and emphasis is placed on whether or not there is a possibility of contact when the host vehicle V1 changes lanes. For this reason, the process of step S112 is performed. That is, assuming that the system according to this embodiment is operated on a highway, when the speed of the adjacent vehicle V2 is less than 10 km / h, even when the adjacent vehicle V2 exists, when changing the lane, Since it is located far behind the host vehicle V1, there is little problem. Similarly, when the relative movement speed of the adjacent vehicle V2 with respect to the own vehicle V1 exceeds +60 km / h (that is, when the adjacent vehicle V2 moves at a speed higher than 60 km / h than the speed of the own vehicle V1), the lane When changing, it is less likely to cause a problem because the vehicle is moving in front of the host vehicle V1. For this reason, in step S112, it can be said that the adjacent vehicle V2 which becomes a problem at the time of lane change is judged.

  Moreover, the following effects are obtained by determining whether the absolute moving speed of the adjacent vehicle V2 is 10 km / h or more and the relative moving speed of the adjacent vehicle V2 with respect to the host vehicle V1 is +60 km / h or less in step S112. . For example, depending on the mounting error of the camera 10, the absolute moving speed of the stationary object may be detected to be several km / h. Therefore, by determining whether the speed is 10 km / h or more, it is possible to reduce the possibility of determining that the stationary object is the adjacent vehicle V2. Further, depending on the noise, the relative speed of the adjacent vehicle V2 with respect to the host vehicle V1 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 of step S112, it may be determined that the absolute movement speed of the adjacent vehicle V2 is not negative or 0 km / h. Further, in the present embodiment, since emphasis is placed on whether or not there is a possibility of contact when the host vehicle V1 changes lanes, when the adjacent vehicle V2 is detected in step S113, the driver of the host vehicle is notified. A warning sound may be emitted or a display corresponding to a warning may be performed by a predetermined display device.

  Next, the threshold value changing process according to the first embodiment will be described with reference to FIG. FIG. 18 is a flowchart showing the threshold value changing process according to the first embodiment. The threshold change process described below is performed in parallel with the adjacent vehicle detection process shown in FIG. 17, and the difference threshold th set by this threshold change process is the difference threshold in the adjacent vehicle detection process shown in FIG. It will be applied as th.

  As shown in FIG. 18, first, in step S201, the headlight detector 36 detects the headlight of the adjacent vehicle V2. Specifically, the headlight detection unit 36 corresponds to an image area in which the brightness difference from the surroundings is a predetermined value or more and a size greater than or equal to a predetermined value as a light source of the headlight of the adjacent vehicle V2. Detect as candidate area. Further, the headlight detector 36 distinguishes between the headlight of the adjacent vehicle V2 and a light source such as a streetlight in the vehicle width direction from the host vehicle V1 to the candidate area, or from the camera 10 to the candidate area. Based on the rear distance, the headlight of the adjacent vehicle V2 is detected by specifying an image region corresponding to the headlight of the adjacent vehicle V2 from among the plurality of candidate regions.

  In step S202, the threshold value changing unit 39 determines whether or not the headlight of the adjacent vehicle V2 is detected in step S201. If the headlight of the adjacent vehicle V2 is detected, the process proceeds to step S203. If the headlight of the adjacent vehicle V2 is not detected, the process returns to step S201, and the headlight of the adjacent vehicle V2 is detected again. Repeated.

  In step S203, the headlight vehicle speed calculation unit 37 calculates the headlight vehicle speed of the adjacent vehicle V2. For example, the headlight vehicle speed calculation unit 37 calculates the moving distance of the headlight at a predetermined time as the moving distance of the adjacent vehicle V2 based on the change in the position of the headlight of the adjacent vehicle V2 detected in step S201. A relative vehicle speed of the adjacent vehicle V2 with respect to the host vehicle V1 is calculated as a headlight vehicle speed VELhl by differentiating the calculated moving distance of the adjacent vehicle V2 with respect to time.

  In the subsequent step S204, the speed difference calculation unit 38 acquires the differential vehicle speed. For example, the speed difference calculation unit 38 acquires the difference vehicle speed calculated in the adjacent vehicle detection process illustrated in FIG. 17 from the difference vehicle speed calculation unit 35.

  In step S205, the speed difference calculation unit 38 calculates the speed difference evaluation value VELdif based on the headlight vehicle speed VELhl calculated in step S203 and the differential vehicle speed VELsa acquired in step S204 according to the above mathematical formula 1. Is done. Specifically, the speed difference calculation unit 38 normalizes the difference between the headlight vehicle speed VELhl and the difference vehicle speed VELsa with the vehicle speed VELv1 of the host vehicle V1, and calculates the difference between the normalized headlight vehicle speed VELhl and the difference vehicle speed VELsa. The speed difference evaluation value VELdif is calculated. The speed difference evaluation value VELdif calculated in step S205 is stored in a memory (not shown) of the computer 30.

In step S206, the speed difference calculation unit 38 calculates the average value VELdif _ave of the speed difference evaluation values calculated in step S205. In this embodiment, every time the headlight of the adjacent vehicle V2 is detected, the speed difference evaluation value VELdif in step S205 is calculated, and the calculated speed difference evaluation value VELdif is stored in a memory (not shown) of the computer 30. Is done. In step S206, the speed difference calculation unit 38 calculates an average value VELdif _ave of the speed difference evaluation values based on the speed difference evaluation values VELdif _ave for a predetermined number (for example, 10) stored in the memory.

In step S207, based on the average value VELdif _ave of the speed difference evaluation value calculated in step S206 by the threshold changing unit 39, the degree of contamination of the lens due to foreign matters such as raindrops attached to the lens is determined as the lens state determination value. Calculated. Specifically, as shown in FIG. 15, the threshold value changing unit 39 increases the average value VELdif _ave of the speed difference evaluation value based on the difference between the headlight vehicle speed VELhl and the difference vehicle speed VELsa, so that the foreign matter such as raindrops on the lens. The lens state determination value is calculated to be high. Further, the threshold value changing unit 39 determines whether or not it is raining, and when it is determined that it is raining, as illustrated in FIG. 15, the speed difference is set so that the lens state determination value is calculated high. The relationship between the average value VELdif _ave of the evaluation values and the lens state determination value is changed.

  In step S208, the threshold value changing unit 39 performs a process of changing the difference threshold value th based on the lens state determination value calculated in step S207. Specifically, as shown in FIG. 16, as the lens state determination value is higher, the threshold value changing unit 39 is contaminated with foreign matter such as raindrops on the lens, and the adjacent vehicle V2 is erroneously detected. The difference threshold th for detecting a three-dimensional object (adjacent vehicle V2) is changed to a high value by determining that it is easy. Thus, when the lens state determination value is high and the lens is dirty, the three-dimensional object is detected using the changed difference threshold th in the adjacent vehicle detection process shown in FIG. As a result, it is possible to effectively prevent foreign objects such as raindrops and three-dimensional objects other than the adjacent vehicle V2 from being erroneously detected as the adjacent vehicle V2.

In the present embodiment, in view of the fact that it is difficult to calculate the relative vehicle speed of the adjacent vehicle V2 with respect to the host vehicle V1 based on the differential waveform DW _t due to adhesion of foreign matters such as raindrops on the lens. The V2 headlight is detected, and the relative vehicle speed of the adjacent vehicle V2 is estimated based on the headlight of the adjacent vehicle V2. Therefore, the threshold value changing process shown in FIG. 18 can be configured to be performed only at night, for example, under the condition that the adjacent vehicle V2 lights the headlight. Thereby, the calculation load at the time of detecting the adjacent vehicle V2 can be reduced in the daytime. The threshold value changing unit 39 can determine that it is nighttime, for example, when the luminance of the entire captured image captured by the camera 10 is equal to or less than a predetermined value. Further, the threshold value changing unit 39 can also determine whether it is nighttime based on the illuminometer and the time.

  Further, the above-described threshold value changing process may be performed only during rainy weather when there is a high possibility that foreign matter such as raindrops will adhere to the lens. In this case, as described above, the threshold value changing unit 39 can determine whether or not it is raining based on the detection result of the raindrop sensor and the operation state of the wiper.

As described above, in the first embodiment, based on the difference waveform DW _t, it calculates the relative speed of the adjacent vehicle V2 as a differential speed VELsa, detects headlights of an adjacent vehicle V2, the adjacent vehicle V2 headlights Based on the above, the relative speed of the adjacent vehicle V2 is calculated as the headlight vehicle speed VELhl. Then, the speed difference evaluation value VELdif is calculated based on the difference between the differential vehicle speed VELsa and the headlight vehicle speed EVLhl, and the larger the calculated average value VELdif _ave of the speed difference evaluation value, the more the foreign matter such as raindrops attached to the lens. It is determined that the degree of lens contamination (lens state determination value) is high, and the difference threshold th for detecting a three-dimensional object is changed to a high value. As a result, in the first embodiment, even when foreign matter such as raindrops is attached to the lens, foreign matter such as raindrops attached to the lens or a three-dimensional object other than the adjacent vehicle V2 existing in the adjacent lane is set as the adjacent vehicle V2. It is possible to effectively prevent erroneous detection.

  Further, in the present embodiment, as shown in the above equation 1, the difference between the headlight vehicle speed VELhl and the differential vehicle speed VELsa is normalized, and the speed difference evaluation value VELdif is calculated, thereby obtaining the relative vehicle speed of the adjacent vehicle V2. Regardless, the degree of lens contamination (lens state determination value) can be determined appropriately. Further, in the present embodiment, the average value of the speed difference evaluation values for a predetermined number is obtained, and the lens state determination value is calculated based on the average value of the speed difference evaluation values. Value) can be determined more appropriately.

<< Second Embodiment >>
Next, the three-dimensional object detection device 1a according to the second embodiment will be described. As shown in FIG. 19, 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. 19 is a block diagram showing details of the computer 30a according to the second embodiment.

  As shown in FIG. 19, the three-dimensional object detection apparatus 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 40, and an edge line detection unit. 41, an edge waveform generation unit 42, an edge vehicle speed calculation unit 43, a three-dimensional object detection unit 34a, a headlight detection unit 36, a headlight vehicle speed calculation unit 37, a speed difference calculation unit 38a, and a threshold value change unit 39a. Below, each structure of the solid-object detection apparatus 1a which concerns on 2nd Embodiment is demonstrated. Note that the viewpoint conversion unit 31, the headlight detection unit 36, and the headlight vehicle speed calculation unit 37 have the same configuration as that of the first embodiment, and thus description thereof is omitted.

  FIG. 20 is a diagram illustrating an imaging range and the like of the camera 10 of FIG. 19, FIG. 20A is a plan view, and FIG. 20B is a perspective view in real space on the rear side from the host vehicle V1. Show. As shown in FIG. 20A, 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 and L2 of the adjacent vehicle V2 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. 20B, 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. 20A 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. 20B, the detection areas A1 and A2 are true squares (rectangles) in the real space behind the host vehicle V1.

  The luminance difference calculation unit 40 calculates a luminance difference with respect to the bird's-eye view image data subjected to viewpoint conversion by the viewpoint conversion unit 31 in order to detect the edge of the three-dimensional object included in the bird's-eye view image. The luminance difference calculation unit 40 calculates a luminance difference between two pixels in the vicinity of each position for each of a plurality of positions along a vertical imaginary line extending in the vertical direction in the real space. The luminance difference calculation unit 40 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 calculation unit 40 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 40 continuously obtains a luminance difference between a point on the first vertical imaginary line and a point on the second vertical imaginary line along the first vertical imaginary line and the second vertical imaginary line. Hereinafter, the operation of the luminance difference calculation unit 40 will be described in detail.

  As shown in FIG. 21A, the luminance difference calculation unit 40 corresponds to a line segment extending in the vertical direction in real space, and passes through the detection area A1 (hereinafter, attention line La). Set). In addition, unlike the attention line La, the luminance difference calculation unit 40 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 40 sets a point of interest Pa (a point on the first vertical imaginary line) on the line of interest La. The luminance difference calculation unit 40 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. 21B in the real space. As is clear from FIG. 21B, the attention line La and the reference line Lr are lines extending in the vertical direction on 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 40 calculates a luminance difference between the attention point Pa and the reference point Pr. If the luminance difference between the attention point Pa and the reference point Pr is large, it is considered that an edge exists between the attention point Pa and the reference point Pr. 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 41 illustrated in FIG. 19 detects an edge line based on a luminance difference between the attention point Pa and the reference point Pr.

  This point will be described in more detail. FIG. 22 is a diagram illustrating a detailed operation of the luminance difference calculation unit 40, FIG. 22 (a) shows a bird's-eye view image in a bird's-eye view state, and FIG. 22 (b) is shown in FIG. 22 (a). It is the figure which expanded a part B1 of the bird's-eye view image. Although only the detection area A1 is illustrated and described in FIG. 22, 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. 22A in FIG. 22B, 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 40 first sets the reference line Lr. The reference line Lr is set along the vertical direction at a position away from the attention line La by a predetermined distance in the real space. Specifically, in the three-dimensional object detection device 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 40 sets a plurality of attention points Pa1 to PaN on the attention line La. In FIG. 22B, 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 40 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 40 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 40 calculates the brightness | luminance difference of two pixels for every some position (1-N) along the vertical virtual line extended in the perpendicular direction in real space. For example, the luminance difference calculation unit 40 calculates a luminance difference between the first point of interest Pa1 and the first reference point Pr1, and the luminance difference between the second point of interest Pa2 and the second reference point Pr2. Will be calculated. Thereby, the luminance difference calculation unit 40 continuously calculates the luminance difference along the attention line La and the reference line Lr. That is, the luminance difference calculation unit 40 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 40 repeatedly executes the above-described processing such as setting the reference line Lr, setting the attention point Pa and the reference point Pr, and calculating the luminance difference while shifting the attention line La in the detection area A1. That is, the luminance difference calculation unit 40 repeatedly executes the above processing while changing the position 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 40 sets the 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. 19, the edge line detection unit 41 detects an edge line from the continuous luminance difference calculated by the luminance difference calculation unit 40. For example, in the case illustrated in FIG. 22B, the first attention point Pa1 and the first reference point Pr1 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. Therefore, the edge line detection unit 41 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 41 firstly follows the following equation 1 to determine the i-th attention point Pai (coordinates (xi, yi)) and the i-th reference point Pri (coordinates (xi). ', Yi')), the i th attention point Pai is attributed.
[Formula 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 an edge 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. Show. 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”. In the present embodiment, the edge threshold value t may be changed by a threshold value changing unit 39a described later. When the edge threshold value t is changed by the threshold value changing unit 39a, the edge threshold value t is changed by the threshold value changing unit 39a. Using the edge threshold t, the attribute s (xi, yi) of the attention point Pai is detected.

Next, the edge line detection unit 41 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 the following formula 2.
[Formula 2]
When s (xi, yi) = s (xi + 1, yi + 1) (and excluding 0 = 0),
c (xi, yi) = 1
Other than the above
c (xi, yi) = 0

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

  Next, the edge line detection unit 41 obtains a sum for the continuity c of all the points of interest Pa on the line of interest La. The edge line detection unit 41 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 41 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 41 determines whether or not the attention line La is an edge line based on Equation 3 below. Then, the edge line detection unit 41 determines whether or not all the attention lines La drawn on the detection area A1 are edge lines.
[Formula 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. 23 is a diagram illustrating an image example for explaining the processing of the edge line detection unit 41. In this image example, a first striped pattern 101 showing a striped pattern in which a high brightness area and a low brightness area are repeated, and a second striped pattern showing a striped pattern in which a low brightness area and a high brightness area are repeated. 102 is an adjacent image. Further, in this image example, a region where the brightness of the first striped pattern 101 is high and a region where the brightness of the second striped pattern 102 is low are adjacent to each other, and a region where the brightness of the first striped pattern 101 is low and the second striped pattern 102. Is adjacent to a region with high brightness. The portion 103 located at the boundary between the first striped pattern 101 and the second striped pattern 102 tends not to be perceived as an edge depending on human senses.

  On the other hand, since the low luminance region and the high luminance region are adjacent to each other, if the edge is detected only by the luminance difference, the part 103 is recognized as an edge. However, since the edge line detection unit 41 determines the part 103 as an edge line only when the attribute of the luminance difference is continuous in addition to the luminance difference in the part 103, the edge line detection unit 41 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. 19, the three-dimensional object detection unit 34 a detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 41. 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 34 a detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 41. Specifically, the three-dimensional object detection unit 34a determines whether or not the amount of edge lines detected by the edge line detection unit 41 is equal to or greater than a predetermined threshold value β, and the amount of edge lines is equal to the predetermined threshold value β. In the above case, the edge line detected by the edge line detection unit 41 is determined to be an edge line of a three-dimensional object.

  Furthermore, prior to detecting the three-dimensional object, the three-dimensional object detection unit 34a determines whether or not the edge line detected by the edge line detection unit 41 is correct. The three-dimensional object detection unit 34a 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. 24 is a diagram showing the luminance distribution of the edge line, and FIG. 24A shows the edge line and 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. 24A, 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 is extended in the bird's-eye view image by converting the image captured by the camera 10 into the bird's-eye view image. On the other hand, as shown in FIG. 24B, 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 34a determines whether or not the edge line is detected by erroneous determination. For example, when a captured image acquired by the camera 10 is converted into a bird's-eye view image, the three-dimensional object included in the captured image tends to appear in the bird's-eye view image in a stretched state. As described above, when the tire of the adjacent vehicle V2 is stretched, one portion of the tire is stretched, so that the luminance change of the bird's eye view image in the stretched direction tends to be small. On the other hand, when a character or the like drawn on the road surface is erroneously determined as an edge line, the bird's-eye view image includes a high luminance region such as a character portion and a low luminance region such as a road surface portion. In this case, the brightness change in the stretched direction tends to increase in the bird's-eye view image. Therefore, 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 34a detects the edge line by erroneous determination. Judge that it is not caused. 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 34a 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 34a calculates the luminance change of the edge line according to any of the following formulas 4 and 5. The luminance change of the edge line corresponds to the evaluation value in the vertical direction in the real space. Equation 4 below evaluates the luminance distribution by the sum of the squares of the differences between the i-th luminance value I (xi, yi) on the attention line La and the adjacent i + 1-th luminance value I (xi + 1, yi + 1). . Equation 5 below evaluates the luminance distribution by the sum of the absolute values of the differences between the i-th luminance value I (xi, yi) on the attention line La and the adjacent i + 1-th luminance value I (xi + 1, yi + 1). To do.
[Formula 4]
Evaluation value in the vertical equivalent direction = Σ [{I (xi, yi) −I (xi + 1, yi + 1)} ² ]
[Formula 5]
Evaluation value in the vertical equivalent direction = Σ | I (xi, yi) −I (xi + 1, yi + 1) |

Not only the above formula 5, but also the attribute b of the adjacent luminance value is binarized using the threshold value t2 as in the following formula 6, and the binarized attribute b is summed for all the attention points Pa. May be.
[Formula 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 34a 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.

Returning to FIG. 19, the edge waveform generation unit 42 generates a one-dimensional edge waveform EW _t based on the edge lines detected from the portions corresponding to the detection regions A1 and A2. For example, similarly to the generation of the differential waveform DW _t in the first embodiment, the edge waveform generation unit 42 counts the number of pixels corresponding to the edge line along the direction in which the three-dimensional object falls due to the viewpoint conversion to generate the frequency distribution. By doing so, a one-dimensional edge waveform ED _t can be generated.

Edge speed calculation unit 43, like the difference speed calculation unit 35 of the first embodiment, based on the edge waveform EW _t and one unit time before edge waveform EW _t-1 at the current time, the moving speed of the three-dimensional object (Relative vehicle speed of the adjacent vehicle V2 with respect to the host vehicle V1) is calculated as the edge vehicle speed. In other words, the edge vehicle speed calculation unit 43 calculates the movement distance of the three-dimensional object at a predetermined time from the time change of the edge waveforms EW _t and EW _t−1 and differentiates the calculated movement distance of the three-dimensional object with time. The relative moving speed of the three-dimensional object with respect to the vehicle V1 (the relative vehicle speed of the adjacent vehicle V2 with respect to the host vehicle V1) is calculated as the edge vehicle speed.

The speed difference calculation unit 38a calculates the difference between the headlight vehicle speed VELhl calculated by the headlight vehicle speed calculation unit 37 and the edge vehicle speed VELed calculated by the edge vehicle speed calculation unit 43, as shown in Equation 2 below. Normalized by the vehicle speed VELv1 of the vehicle V1, the difference between the normalized headlight vehicle speed VELhl and the edge vehicle speed VELed is calculated as a speed difference evaluation value VELdif.

Further, the speed difference calculation unit 38a repeatedly calculates the speed difference evaluation value VELdif based on the above formula 2 every time the headlight of the adjacent vehicle V2 is detected, so that the predetermined number of adjacent vehicles V2 are obtained. Based on the calculated speed difference evaluation value VELdif, an average value VELdif _ave of the speed difference evaluation value is calculated.

Based on the average value VELdif _ave of the speed difference evaluation values calculated by the speed difference calculation unit 38a, the threshold value changing unit 39a calculates the degree of the attachment of foreign matter such as raindrops to the lens as a lens state determination value, and calculates Based on the determined lens state determination value, the edge threshold value t for detecting the three-dimensional object is changed. Specifically, as in the first embodiment, the threshold value changing unit 39a determines that the foreign matter such as raindrops is attached to the lens as the average value VELdif _ave of the speed difference evaluation value is higher, and the lens state The judgment value is calculated high. Then, the threshold value changing unit 39a changes the edge threshold value t for detecting the adjacent vehicle V2 to a higher value as the lens state determination value is higher in order to prevent erroneous detection of the adjacent vehicle V2.

  Next, an adjacent vehicle detection method according to the second embodiment will be described. FIG. 25 is a flowchart showing details of the adjacent vehicle detection method according to the second embodiment. In FIG. 25, for convenience, processing for the detection area A1 will be described, but the same processing is executed for the detection area A2.

  In step S301, the camera 10 captures a predetermined area specified by the angle of view a and the attachment position, and the computer 30a acquires image data of the captured image P 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, the luminance difference calculation unit 40 sets the attention line La on the detection area A1 in step S303. At this time, the luminance difference calculation unit 40 sets a line corresponding to a line extending in the vertical direction in the real space as the attention line La. Next, in step S304, the luminance difference calculation unit 40 sets a reference line Lr on the detection area A1. At this time, the luminance difference calculation unit 40 sets a reference line Lr that corresponds to a line segment extending in the vertical direction in the real space and is separated from the attention line La by a predetermined distance in the real space.

  Next, the brightness | luminance difference calculation part 40 sets several attention point Pa on attention line La in step S305. At this time, the luminance difference calculation unit 40 sets the attention points Pa as many as not causing a problem when the edge line detection unit 41 detects the edge. In step S306, the luminance difference calculation unit 40 sets the reference point Pr so that the attention point Pa and the reference point Pr are substantially the same height in the real space. Thereby, the attention point Pa and the reference point Pr are arranged in a substantially horizontal direction, and it becomes easy to detect an edge line extending in the vertical direction in the real space.

  Next, in step S307, the luminance difference calculation unit 40 calculates the luminance difference between the attention point Pa and the reference point Pr that have the same height in the real space. Then, the edge line detection unit 41 calculates the attribute s of each attention point Pa based on the luminance difference calculated by the luminance difference calculation unit 40 according to the above equation 1. In the present embodiment, the attribute s of each attention point Pa is calculated using the edge threshold value t for detecting the edge of the adjacent vehicle V2. The edge threshold value t may be changed in a threshold value changing process to be described later in order to suppress detection of a three-dimensional object. When the edge threshold value t is changed, the changed edge threshold value is changed to this step S307. It will be used in.

  Next, in step S308, the edge line detection unit 41 calculates the continuity c of the attribute s of each attention point Pa according to the above equation 2. In step S309, the edge line detection unit 41 determines whether the value obtained by normalizing the total sum of continuity c is greater than the threshold value θ according to the above equation 3. When it is determined that the normalized value is larger than the threshold θ (step S309 = Yes), the edge line detection unit 41 detects the attention line La as an edge line in step S310. Then, the process proceeds to step S311. If it is determined that the normalized value is not greater than the threshold θ (step S309 = No), the edge line detection unit 41 does not detect the attention line La as an edge line, and the process proceeds to step S311.

  In step S311, the computer 30a determines whether or not the processing in steps S303 to S310 has been executed for all the attention lines La that can be set on the detection area A1. When it is determined that the above processing has not been performed for all the attention lines La (step S311 = No), the processing returns to step S303, a new attention line La is set, and the processing up to step S311 is repeated. On the other hand, if it is determined that the above process has been performed for all the attention lines La (step S311 = Yes), the process proceeds to step S312.

  In step S312, the three-dimensional object detection unit 34a calculates a luminance change along the edge line for each edge line detected in step S310. The three-dimensional object detection unit 34a calculates the luminance change of the edge line according to any one of the above formulas 4, 5, and 6. Next, in step S313, the three-dimensional object detection unit 34a 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 34a 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 present in the adjacent vehicle. .

  Next, in step S314, the three-dimensional object detection unit 34a determines whether or not the amount of the edge line is equal to or greater than a predetermined threshold value β. Here, the threshold value β is a value set in advance by experiments or the like. For example, when a four-wheeled vehicle is set as a three-dimensional object to be detected, the threshold value β is determined in advance by an experiment or the like. It is set based on the number of edge lines of the four-wheeled vehicle that appeared in A1. When it is determined that the amount of the edge line is equal to or larger than the threshold value β (step S314 = Yes), the three-dimensional object detection unit 34a determines that a three-dimensional object exists in the detection area A1, and proceeds to step S315. It is determined that a vehicle exists. On the other hand, when it is determined that the amount of the edge line is not equal to or larger than the threshold β (step S314 = No), the three-dimensional object detection unit 34a determines that there is no three-dimensional object in the detection area A1, and proceeds to step S316. It is determined that there is no adjacent vehicle in the detection area A1.

  Subsequently, the threshold value changing process according to the second embodiment will be described with reference to FIG. Note that the threshold value changing process according to the second embodiment is also performed in parallel with the adjacent vehicle detection process shown in FIG. 25, as in the first embodiment. Further, the threshold value changing process according to the second embodiment is an edge threshold value for detecting a three-dimensional object in order to appropriately prevent erroneous detection of the adjacent vehicle V2 when foreign matter such as raindrops adheres to the lens. t is changed. Therefore, the edge threshold value t changed in this threshold value changing process is used when detecting the edge of the three-dimensional object in the adjacent vehicle detection process shown in FIG.

  As shown in FIG. 26, in steps S401 to S403 of the threshold value changing process according to the second embodiment, the headlight of the adjacent vehicle V2 is detected as in steps S201 to S203 of the first embodiment (step S401). ), It is determined whether or not the headlight of the adjacent vehicle V2 is detected (step S402). When the headlight of the adjacent vehicle V2 is detected (step S402 = Yes), the headlight vehicle speed of the adjacent vehicle V2 is detected (step S403), while the headlight of the adjacent vehicle V2 is not detected. In that case (step S402 = No), the process returns to step S401, and the detection of the headlight of the adjacent vehicle V2 is repeated.

Further, in step S404, an edge waveform EW _t is generated by the edge waveform generation unit 42, and thereafter, based on the edge waveforms EW _t and EW _t−1 generated at different times by the edge vehicle speed calculation unit 43. The relative vehicle speed of the adjacent vehicle V2 with respect to the vehicle V2 is calculated as the edge vehicle speed VELed.

In step S405, the speed difference calculation unit 38a calculates the speed difference evaluation value VELdif on the basis of the headlight vehicle speed VELhl calculated in step S403 and the edge vehicle speed VELed calculated in step S404 according to the above formula 2. Is done. Specifically, the speed difference calculation unit 38a normalizes the difference between the headlight vehicle speed VELhl and the edge vehicle speed VELed by the vehicle speed VELv1 of the host vehicle V1, and calculates the difference between the normalized headlight vehicle speed VELhl and the edge vehicle speed VELed. The speed difference evaluation value VELdif is calculated. In step S406, a speed difference evaluation value VELdif for a predetermined number (for example, 10 units) stored in the memory of the computer 30 based on the speed difference evaluation value VELdif calculated in step S405 by the speed difference calculation unit 38a. Based on _ave , the average value VELdif _ave of the speed difference evaluation values is calculated.

In step S407, based on the average value VELdif _ave of the speed difference evaluation value calculated in step S406 by the threshold changing unit 39a, the degree of contamination of the lens due to foreign matters such as raindrops attached to the lens is determined as the lens state determination value. Calculated.

  In step S408, the threshold value changing unit 39a changes the edge threshold value t based on the lens state determination value calculated in step S407. Specifically, the threshold value changing unit 39a determines that foreign matter such as raindrops adheres to the lens and the lens is dirty as the lens state determination value is higher, and prevents erroneous detection of the adjacent vehicle V2. The edge threshold t for detecting the three-dimensional object (adjacent vehicle V2) is changed to a high value.

As described above, in the second embodiment, the headlight of the adjacent vehicle V2 is detected, and the relative speed of the adjacent vehicle V2 is calculated as the headlight vehicle speed VELhl based on the headlight of the adjacent vehicle V2. An edge waveform EW _t is generated based on the detected edge, and the relative vehicle speed of the adjacent vehicle V2 is calculated as the edge vehicle speed VELed based on the generated edge waveform EW _t . Then, the speed difference evaluation value VELdif is calculated based on the difference between the edge vehicle speed VELed and the headlight vehicle speed EVLhl, and the larger the calculated average value VELdif _ave of the speed difference evaluation value, the more the foreign matter such as raindrops attached to the lens. It is determined that the degree of lens contamination (lens state determination value) is high, and the edge threshold t for detecting a three-dimensional object is changed to a high value. As a result, even in the second embodiment, even when foreign matter such as raindrops is attached to the lens, foreign matter such as raindrops attached to the lens or a three-dimensional object other than the adjacent vehicle V2 is erroneously detected as the adjacent vehicle V2. Can be effectively prevented.

«Third embodiment»
Next, the three-dimensional object detection device 1b according to the third embodiment will be described. As shown in FIG. 27, the three-dimensional object detection device 1b according to the third embodiment includes a computer 30b instead of the computer 30 of the first embodiment, and operates as described below. This is the same as in the first embodiment. FIG. 27 is a block diagram showing details of the computer 30b according to the third embodiment.

  In the third embodiment, as shown in FIG. 27, a foreign object detection unit 44 is provided instead of the threshold value changing unit 39 of the first embodiment. The foreign matter detection unit 44 detects foreign matter such as mud adhering to the lens based on the captured image taken by the camera 10. In the following, with reference to FIG. 28A and FIG. 28B, a foreign object detection method by the foreign object detection unit 44 will be described. 28A and 28B are diagrams for explaining a foreign object detection method by the foreign object detection unit 44. FIG.

Specifically, as shown in FIG. 28A, the foreign object detection unit 44 first blocks or attenuates a high-frequency component with respect to the differential waveform DW _t generated at a predetermined first timing t1 by the differential waveform generation unit 33. Low pass filter (high cut filter) processing is performed. In this way, the differential waveform DW _t can be smoothed and averaged by performing the low-pass filter process on the differential waveform DW _t . In other words, by performing low-pass filtering on the difference waveform DW _t, the differential waveform DW _t, to remove the small maximum value it can be determined that the noise, the maximum value indicating a relatively large change by actualized was obtained Features of image information can be extracted. As a result, the maximum value of the differential waveform DW _t caused by the presence of foreign matter attached to the lens can be made obvious, and the feature of the image information corresponding to the foreign matter can be extracted.

Further, as shown in FIG. 28B, the foreign object detection unit 44 calculates the maximum value of the differential waveform DW _t after the low-pass filter processing as a reference frequency, and a determination range for determining a foreign object based on the reference frequency Set. For example, the foreign object detection unit 44 sets a range from a value obtained by adding a predetermined margin value to the reference frequency to a value obtained by subtracting the predetermined margin value from the reference frequency as the determination range. Incidentally, the reference power is not limited to the maximum value of the differential waveform DW _t after low-pass filtering, for example, a predetermined value by more than a maximum value of the differential waveform DW _t, the differential waveform DW _t after low-pass filtering It can be calculated based on the local maximum value.

Then, as shown in FIG. 28B, the foreign object detection unit 44 uses the maximum value of the differential waveform DW _t newly generated at one or more second timings t2 after the first timing t1 as the evaluation frequency. And the number of times that the difference between the evaluation frequency and the reference frequency having the same position on the bird's-eye view image is determined to be within the determination range is counted up. Then, the foreign matter detection unit 44 repeats the foreign matter detection process within a predetermined observation time defined in advance, and when the number of times counted up is equal to or greater than the predetermined number tc, the result of the count up is derived. It is determined that an image including pixels corresponding to the evaluation frequency is a foreign matter attached to the lens.

  In this way, the foreign object detection unit 44 counts up the number of times that the difference between the evaluation power and the reference power is determined to be within the determination range on the bird's-eye view image, so that the lens such as mud adhered to the lens It is possible to detect a foreign substance that is stuck to and does not move.

Further, in the present embodiment, the foreign matter detection unit 44 detects foreign matter attached to the lens based on the average value VELdif _ave of the speed difference evaluation values calculated by the speed difference calculation unit 38. Specifically, the foreign matter detection unit 44 determines that the larger the average value VELdif _ave of the speed difference evaluation value is, the higher the possibility that foreign matter is attached to the lens, and detects the foreign matter attached to the lens. In order to facilitate the determination, the number of determinations tc for detecting a foreign object is changed to a low value. As a result, the larger the average value VELdif _ave of the speed difference evaluation value and the greater the degree of contamination of the lens (lens state determination value), the easier it is to detect foreign matter, and the foreign matter detection unit 44 can detect foreign matter with higher accuracy. Can be done.

And the detection result of the foreign material by the foreign material detection part 44 is transmitted to the solid object detection part 34b shown in FIG. 27, and is used for the detection of the adjacent vehicle V2 by the solid object detection part 34b. Specifically, when the foreign object detection unit 44 detects a foreign object attached to the lens, the three-dimensional object detection unit 34b uses the difference waveform DW _t corresponding to the image area where the foreign object is detected to be detected by the adjacent vehicle V2. In the detection areas A1 and A2, the detection of the three-dimensional object is suppressed in the area where the foreign matter is present. Thereby, when the foreign material has adhered to the lens, it can suppress effectively that the foreign material adhering to the lens is erroneously detected as the adjacent vehicle V2.

  Note that the processing of the foreign matter detection unit 44 described above can also be performed based on edge information. The foreign object detection unit 44 extracts the first maximum value from the edge information including the edge line information generated by the three-dimensional object detection unit 33 at one or a plurality of first timings, and based on the first maximum value. Get the reference edge length. The edge line information indicates a luminance difference equal to or greater than a predetermined threshold, and includes edge length information (including the number of pixels) having a predetermined continuity. In addition, the foreign object detection unit 44 obtains a second maximum value corresponding to the first maximum value on the bird's-eye view image from the edge information newly generated at one or a plurality of second timings after the first timing. While extracting, evaluation edge length is acquired based on this 2nd maximum value. Then, based on the change with time of the difference between the evaluation edge length and the reference edge length, it is detected whether or not a foreign substance is attached to the lens. The significance of “temporal change” is in common with the significance of “temporal change” in processing based on differential waveform information.

  When it is determined that the degree of change with time of the difference between the evaluation edge length and the reference edge length is within a predetermined determination range, the foreign object detection unit 44 displays an image including pixels corresponding to the evaluation edge length. It is determined that the image is caused by the foreign matter attached to the lens, and it is detected that the foreign matter is attached to the lens.

  Specifically, the foreign object detection unit 44 performs signal processing using at least a bandpass filter on the edge information including the edge line information generated at the first timing, and the “maximum of the reference edge information” after this signal processing is performed. The “reference edge length” is acquired based on the “value”. Then, “evaluation edge length” is acquired based on the maximum value of the edge information newly generated at one or a plurality of second timings after the first timing, and the position on the bird's-eye view image is common. Based on the number of times that the difference between the evaluation edge length and the reference edge length is determined to be within the “predetermined determination range”, an image including a pixel corresponding to the evaluation edge length is caused by a foreign object attached to the lens. Judged to be an image. This determination can be made within a predetermined evaluation time defined in advance. The point that the low-pass filter can be used as the band-pass filter and its action / effect, and that the cutoff / attenuation frequency band of the band-pass filter can be changed according to the detection state of the foreign substance and the action / effect are the same as described above. The explanation is incorporated. The “reference edge information” in the foreign object detection processing based on the edge information corresponds to the “reference difference waveform information” described above, the “reference edge length” corresponds to the “reference value” described above, and the “evaluation edge length”. ”Is the above“ evaluation target value ”, and“ predetermined judgment range ”for evaluating the“ evaluation edge length ”is the“ evaluation target value ”in the process using the reference waveform information described above. This corresponds to the “predetermined determination range”.

As described above, the three-dimensional object detection device 1b according to the third embodiment includes the foreign matter detection unit 44 that detects foreign matter attached to the lens, and averages the speed difference evaluation values based on the difference between the headlight vehicle speed and the difference vehicle speed. The larger the value VELdif _ave is, the more the lens is determined to be dirty, and the predetermined number of times tc for detecting a foreign object is changed to be low so that the foreign object detector 44 can easily detect the foreign object. As a result, the larger the average value VELdif _ave of the speed difference evaluation value and the greater the degree of contamination of the lens (lens state determination value), the easier it is to detect foreign matter. As described above, in the third embodiment, it is possible to detect a foreign object with high accuracy according to the lens state.

  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 first and second embodiments described above, in order to prevent erroneous detection of the adjacent vehicle V2, the difference threshold th or the edge threshold t is changed to a higher value as the lens state determination value is higher. However, the present invention is not limited to this configuration. For example, a threshold value α or a threshold value for detecting a three-dimensional object (adjacent vehicle V2) as the lens state determination value increases in order to prevent erroneous detection of the adjacent vehicle V2. It is good also as a structure which changes (beta) to a high value. Similarly, in order to prevent erroneous detection of the adjacent vehicle V2, the threshold value θ and the threshold value t2 for detecting the edge line may be changed to higher values as the lens state determination value is higher. Furthermore, in order to prevent erroneous detection of the adjacent vehicle V2, the threshold value tb may be changed to a low value.

  For example, in the above-described embodiment, the configuration in which the difference threshold th or the edge threshold t is changed to a higher value as the lens state determination value indicating the degree of foreign matter such as raindrops attached to the lens is increased. Without being limited to this configuration, for example, the higher the lens state determination value, the higher the threshold α for detecting a three-dimensional object, the threshold β may be changed to a higher value, the threshold θ for detecting an edge line, The threshold value t2 may be changed to a high value. As a result, when foreign objects such as raindrops are attached to the lens, detection of a three-dimensional object is suppressed, and therefore, three-dimensional objects other than the foreign object such as raindrops attached to the lens and the adjacent vehicle V2 existing in the adjacent lane. Can be effectively prevented from being erroneously detected as the adjacent vehicle V2. Further, the pixel value (or luminance value) output from the camera 10 may be lowered as the lens state determination value is higher. In this case, since it is difficult to detect the difference pixel DP and the edge, detection of the three-dimensional object (adjacent vehicle V2) is suppressed, and erroneous detection of the adjacent vehicle V2 can be effectively prevented.

  Furthermore, in the above-described embodiment, the configuration in which the three-dimensional object is detected as the adjacent vehicle V2 when the moving speed of the three-dimensional object satisfies the predetermined condition is exemplified. However, the configuration is not limited to this configuration. It is good also as a structure which suppresses the detection of the adjacent vehicle V2 by making said conditions severe, so that is high. For example, in the above-described embodiment, when the absolute moving speed of the three-dimensional object is 10 km / h or more and the relative moving speed of the three-dimensional object with respect to the host vehicle V1 is +60 km / h or less, the three-dimensional object is determined as the adjacent vehicle V2. However, when the lens state determination value is high, for example, when the absolute moving speed of the three-dimensional object is 20 km / h or more and the relative moving speed of the three-dimensional object with respect to the host vehicle V1 is +50 km / h or less. It can be determined that the three-dimensional object is the adjacent vehicle V2.

In the first embodiment described above, when the speed difference evaluation value VELdif is calculated based on the headlight vehicle speed VELhl and the differential vehicle speed VELsa, the headlight vehicle speed VELhl and the differential vehicle speed VELsa are calculated as shown in Equation 1 above. However, the present invention is not limited to this configuration. For example, when calculating the speed difference evaluation value VELdif, as shown in the following Equation 3, A difference between the headlight vehicle speed VELhl and the difference vehicle speed VELsa may be normalized by the headlight vehicle speed VELhl. Alternatively, as shown in Equation 4 below, when the speed difference evaluation value VELdif is calculated, the difference between the headlight vehicle speed VELhl and the differential vehicle speed VELsa is normalized with the average value of the headlight vehicle speed VELhl and the differential vehicle speed VELsa. It is good also as composition to do. Similarly, in the second embodiment described above, when the speed difference evaluation value VELdif is calculated, the difference between the headlight vehicle speed VELhl and the edge vehicle speed VELed may be normalized by the headlight vehicle speed VELhl. It is good also as a structure normalized by the average value of headlight vehicle speed VELhl and edge vehicle speed VELed.

  Further, in addition to the above-described embodiment, when calculating the speed difference evaluation value VELdif, it is determined whether or not the host vehicle V1 is overtaken by the adjacent vehicle V2, and the speed difference evaluation value is determined according to the determination result. It may be configured to calculate VELdif. For example, by determining whether the difference vehicle speed or the headlight vehicle speed VELhl is a positive value or a negative value, it is determined whether the host vehicle V1 is overtaken by the adjacent vehicle V2, and the host vehicle V1 is When it is determined that the vehicle V1 is overtaken by the adjacent vehicle V2, the speed difference evaluation value VELdif may be calculated at a lower value than when it is determined that the host vehicle V1 is overtaking the adjacent vehicle V2. . For example, since a stationary object such as a street lamp does not move, the relative moving speed of the stationary object with respect to the host vehicle V1 is 0 or less, while a three-dimensional object having a relative moving speed faster than the host vehicle V1 may be the adjacent vehicle V2. Can be judged to be high. That is, compared with the case where the host vehicle V1 is overtaking the adjacent vehicle V2, the possibility that the adjacent vehicle V2 is erroneously detected is low when the host vehicle V1 is overtaken by the adjacent vehicle V2. By calculating the difference evaluation value VELdif as a low value and not setting the difference threshold th too high, it is possible to appropriately detect the adjacent vehicle V2. Similarly, the speed difference evaluation value VELdif may be calculated and the difference threshold th may be changed only when it is determined that the host vehicle V1 has overtaken the adjacent vehicle V2.

Furthermore, in the above-described embodiment, the average value VELdif _ave of the speed difference evaluation value for a predetermined number of units is calculated by repeatedly calculating the speed difference evaluation value VELdif, but the average value VELdif of the speed difference evaluation value is exemplified. When calculating _ave , it is determined whether the host vehicle V1 is a scene overtaking the adjacent vehicle V2 or the host vehicle V1 is being overtaken by the adjacent vehicle V2, and according to the determination result The speed difference evaluation value VELdif may be weighted. Specifically, the speed difference evaluation value obtained when the host vehicle V1 is overtaken by the adjacent vehicle V2 rather than the speed difference evaluation value VELdif obtained when the host vehicle V1 is overtaking the adjacent vehicle V2. It is also possible to increase the weight of VELdif and calculate the average value VELdif _ave of the speed difference evaluation values for a predetermined number of weighted vehicles (for example, the latest 10 vehicles). In the scene where the host vehicle V1 is overtaken by the adjacent vehicle V2, there is a low possibility that the adjacent vehicle V2 is erroneously detected. Therefore, the speed difference evaluation value is compared with the scene where the host vehicle V1 is overtaking the adjacent vehicle V2. By calculating VELdif with a low value, the adjacent vehicle V2 can be detected appropriately.

Furthermore, in the third embodiment described above, on the basis of the difference between the headlight vehicle speed VELhl and differential speed VELsa, but illustrating the configuration of calculating the average value VELdif _ave speed difference evaluation value is not limited to this configuration, For example, instead of the differential vehicle speed VELsa, the speed difference evaluation value VELdif is calculated using the edge vehicle speed VELed, and the average value VELdif _ave of the speed difference evaluation value is calculated based on the difference between the headlight vehicle speed VELhl and the edge vehicle speed VELed. It is good also as composition to do. In this case, the same effect as that of the third embodiment can be obtained.

  Further, in the third embodiment described above, the configuration in which, when the foreign object detection unit 44 detects a foreign object attached to the lens, excludes the area where the foreign object is detected from the detection target of the adjacent vehicle V2, For example, when a foreign object attached to the lens is detected by the foreign object detection unit 44, a foreign object is detected in the detection areas A1 and A2 so that the detected foreign object is not erroneously detected as the adjacent vehicle V2. The difference threshold th and the edge threshold t of the region that has been set may be changed to a high value.

Furthermore, in the third embodiment described above, as shown in FIG. 28B, the number of times that the difference between the evaluation frequency and the reference frequency having the same position on the bird's-eye view image is determined to be within the determination range is counted up. In the case where the number of times counted up is equal to or greater than the predetermined number tc, an example of a configuration in which an image including a pixel corresponding to the evaluation frequency that has led to the count-up result is determined to be a foreign matter attached to the lens is illustrated. However, the present invention is not limited to this configuration. For example, the determination value according to the difference between the evaluation frequency and the reference frequency having the same position on the bird's-eye view image is counted, and the integrated value of the counted determination value is a predetermined threshold value. When it becomes above, it is good also as a structure which judges that the image containing the pixel corresponding to the evaluation frequency which led the count-up result is the foreign material adhering to the lens. In this case, as the average value VELdif _ave of the speed difference evaluation value is larger, the determination value corresponding to the difference between the evaluation frequency and the reference frequency having a common position on the bird's-eye view image is calculated or counted up. By reducing the predetermined threshold value for determining the integrated value, the foreign matter detection unit 44 can easily detect the foreign matter as the average value VELdif _ave of the speed difference evaluation value increases.

Further, in addition to the above-described embodiment, when detecting raindrops attached to the lens based on the operation state of the raindrop sensor or the wiper, the larger the average value VELdif _ave of the speed difference evaluation value is, the more the raindrops are on the lens. It is good also as a structure which makes it easy to determine with adhering.

  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, the difference waveform generation unit 33, and the luminance difference calculation unit 40. The edge line detector 41, the edge vehicle speed calculator 43, and the three-dimensional object detectors 34 and 34a correspond to the three-dimensional object detector of the present invention, and the three-dimensional object detectors 34 and 34a correspond to the three-dimensional object determiner of the present invention. The headlight detection unit 36 corresponds to the light source detection unit of the present invention, the headlight vehicle speed calculation unit 37 corresponds to the second movement speed calculation unit of the present invention, and the speed difference calculation units 38 and 38a and the threshold value change unit 39 are used. Corresponds to the control means and determination means of the present invention, and the foreign object detector 44 corresponds to the foreign object detection means of the present invention.

DESCRIPTION OF SYMBOLS 1, 1a ... Three-dimensional object detection apparatus 10 ... Camera 20 ... Vehicle speed sensor 30, 30a ... Calculator 31 ... Viewpoint conversion part 32 ... Position alignment part 33 ... Difference waveform generation part 34, 34a ... Three-dimensional object detection part 35 ... Difference vehicle speed calculation part 36 ... Headlight detection unit 37 ... Headlight vehicle speed calculation unit 38, 38a ... Speed difference calculation unit 39, 39a ... Threshold change unit 40 ... Luminance difference calculation unit 41 ... Edge line detection unit 42 ... Edge waveform generation unit 43 ... Edge vehicle speed calculator 44 ... foreign object detector a ... angle A1, A2 ... detection area CP ... intersection DP ... differential pixel _{DW t, DW} t _'... differential waveform _{DW t1 ~DW m, DW m +} k ~DW tn ... small regions L1, L2 ... ground lines La, Lb ... line P in the direction in which the three-dimensional object falls ... captured image PB _t ... bird's-eye view image PD _t ... difference image V1 ... own vehicle V2 ... adjacent vehicle

Claims (13)

An imaging means equipped with a lens mounted on the vehicle and forming an image of the rear of the host vehicle;
Image conversion means for converting the captured image obtained by the imaging means into a bird's eye view image;
The position of the bird's-eye view image at different times obtained by the image conversion means is aligned on the bird's-eye view, and the number of pixels indicating a predetermined difference is counted on the difference image of the aligned bird's-eye view image. Difference waveform information is generated by distribution, a three-dimensional object is detected based on the difference waveform information, and a moving speed of the three-dimensional object is calculated as a first moving speed from a time change of the waveform of the difference waveform information. Three-dimensional object detection means;
A three-dimensional object determination means for determining whether the three-dimensional object detected by the three-dimensional object detection means is an adjacent vehicle existing in an adjacent lane based on the first moving speed of the three-dimensional object;
Light source detection means for detecting a light source present behind the host vehicle based on a captured image obtained by the imaging means;
Second moving speed calculation means for calculating a moving speed of the three-dimensional object as a second moving speed based on a time change of the light source detected by the light source detecting means;
As the difference between the first moving speed and the second moving speed is larger, the detection of the three-dimensional object is suppressed based on the difference waveform information, or the three-dimensional object is determined as the adjacent vehicle. And a control means for suppressing the three-dimensional object detection device. The three-dimensional object detection device according to claim 1,
The three-dimensional object detection means 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 changes the value of the first threshold value or the second threshold value to a higher value as the difference between the first movement speed and the second movement speed is larger, based on the difference waveform information. A three-dimensional object detection device that suppresses detection of the three-dimensional object. The three-dimensional object detection device according to claim 1 or 2,
When the three-dimensional object detection unit generates the difference waveform information, the control unit counts the number of pixels indicating a predetermined difference on the difference image to the three-dimensional object detection unit to obtain a frequency distribution value. A three-dimensional object detection device that suppresses detection of the three-dimensional object based on the differential waveform information by outputting a low voltage. The three-dimensional object detection device according to any one of claims 1 to 3,
A first maximum value is extracted from the differential waveform information generated at one or a plurality of first timings by the three-dimensional object detection means, a reference value is acquired based on the extracted first maximum value, and the first Extracting a second maximum value corresponding to the first maximum value on the bird's-eye view image from the differential waveform information newly generated at one or a plurality of second timings after the first timing; An evaluation target value is acquired based on a bi-maximal value, and when an index based on a change over time in the difference between the evaluation target value and the reference value is equal to or greater than a predetermined third threshold, foreign matter adheres to the lens. A foreign matter detection means for detecting whether or not foreign matter is attached to the lens by determining that
The control unit suppresses detection of the three-dimensional object based on the difference waveform information when the foreign object detection unit detects a foreign object attached to the lens, or the solid object is detected as the adjacent vehicle. To suppress the judgment,
The control means promotes detection of foreign matter by the foreign matter detection means by changing the third threshold value to a lower value as the difference between the first movement speed and the second movement speed is larger. A three-dimensional object detection device. An imaging means equipped with a lens mounted on the vehicle and forming an image of the rear of the host 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 based on the bird's-eye view image obtained by the image conversion means, a solid object is detected based on the edge information, and the moving speed of the solid object is first moved from the time change of the edge information. A three-dimensional object detection means for calculating the speed;
A three-dimensional object determination means for determining whether the three-dimensional object detected by the three-dimensional object detection means is an adjacent vehicle existing in an adjacent lane based on the edge information;
Light source detection means for detecting a light source present behind the host vehicle based on a captured image obtained by the imaging means;
Second moving speed calculation means for calculating a moving speed of the three-dimensional object as a second moving speed based on a time change of the light source detected by the light source detecting means;
The greater the difference between the first movement speed and the second movement speed, the more the detection of the solid object is suppressed based on the edge information, or the determination of the solid object as the adjacent vehicle is suppressed. A three-dimensional object detection apparatus comprising: The three-dimensional object detection device according to claim 5,
The three-dimensional object detection means detects an edge component whose luminance difference between adjacent pixel areas is not less than a predetermined first threshold value from the bird's eye view image, and the amount of the edge information based on the edge component is a predetermined second value. When the threshold is equal to or greater than the threshold, the solid object is detected based on the edge information,
The control means changes the value of the first threshold value or the second threshold value to a higher value as the difference between the first movement speed and the second movement speed is larger, and thereby based on the edge information. A three-dimensional object detection device that suppresses detection of a three-dimensional object or suppresses determination of the three-dimensional object as the adjacent vehicle. The three-dimensional object detection device according to claim 5 or 6,
The control means detects the solid object based on the edge information by causing the solid object detection means to output the edge information low when the solid object detection means detects the edge information. A three-dimensional object detection device characterized by suppressing. The three-dimensional object detection device according to any one of claims 5 to 7,
A first maximum value is extracted from the edge information detected by the three-dimensional object detection means at one or a plurality of first timings, a reference edge length is acquired based on the first maximum value, and the first And extracting a second maximum value corresponding to the first maximum value on the bird's-eye view image from the edge information newly generated at one or a plurality of second timings after the second timing, and the second maximum The evaluation edge length is acquired based on the value, and when the index based on the change over time of the difference between the evaluation edge length and the reference edge length is equal to or greater than a predetermined third threshold, foreign matter adheres to the lens. A foreign matter detection means for detecting whether or not foreign matter is attached to the lens by determining that
The control unit suppresses detection of the three-dimensional object based on the edge information when the foreign object detection unit detects a foreign object attached to the lens, or the solid object is detected as the adjacent vehicle. Suppress judgment,
The control means promotes detection of foreign matter by the foreign matter detection means by changing the third threshold value to a lower value as the difference between the first movement speed and the second movement speed is larger. A three-dimensional object detection device. The three-dimensional object detection device according to any one of claims 1 to 8,
The control means normalizes a difference between the first movement speed and the second movement speed, and detects the three-dimensional object based on the normalized difference between the first movement speed and the second movement speed. to suppress, or three-dimensional object detecting apparatus characterized by suppressing to determine the three-dimensional object and the adjacent vehicle. The three-dimensional object detection device according to claim 1,
Based on the first moving speed or the second moving speed, further comprising a judging means for judging whether or not the host vehicle is overtaking the three-dimensional object;
The control means increases the degree of suppression when suppressing the determination that the three-dimensional object is the adjacent vehicle when the determination means determines that the host vehicle is overtaking the three-dimensional object. A three-dimensional object detection device characterized by suppressing. The three-dimensional object detection device according to any one of claims 1 to 10,
It further includes a night determination means for determining whether it is night,
The control means suppresses the detection of the three-dimensional object based on the difference between the first movement speed and the second movement speed only when the night determination means determines that it is night. Alternatively, the three-dimensional object detection device is configured to suppress the determination of the three-dimensional object as the adjacent vehicle. A captured image obtained by imaging the rear of the host vehicle is converted into a bird's-eye view image, the position of the bird's-eye view image at a different time is aligned on the bird's-eye view, and a predetermined image is displayed on the difference image of the aligned bird's-eye view image. The differential waveform information is generated by counting the number of pixels indicating the difference and frequency distribution, and detecting the three-dimensional object based on the difference waveform information, and from the time change of the waveform of the difference waveform information, A three-dimensional object detection method for calculating a moving speed as a first moving speed and determining whether the three-dimensional object is an adjacent vehicle existing in an adjacent lane based on the first moving speed,
A light source existing behind the host vehicle is detected, and a moving speed of the three-dimensional object is calculated as a second moving speed based on a temporal change of the light source, and a difference between the first moving speed and the second moving speed is large. The solid object detection method is characterized by suppressing the detection of the solid object based on the difference waveform information, or suppressing the determination of the solid object as the adjacent vehicle. The captured image obtained by imaging the rear of the host vehicle is converted into a bird's-eye view image, edge information is detected based on the bird's-eye view image, a solid object is detected based on the edge information, and based on the edge information, A solid object detection method for determining whether or not the solid object is an adjacent vehicle existing in an adjacent lane,
The moving speed of the three-dimensional object is calculated from the time change of the edge information as the first moving speed, the light source existing behind the host vehicle is detected, and the moving speed of the three-dimensional object is determined based on the time change of the light source. Calculated as a moving speed, the greater the difference between the first moving speed and the second moving speed, the more the detection of the three-dimensional object is suppressed based on the edge information, or the three-dimensional object is the adjacent vehicle. The solid-object detection method characterized by suppressing judging that.

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