WO2013121911A1 - Dispositif de détection d'objet solide et procédé de détection d'objet solide - Google Patents

Dispositif de détection d'objet solide et procédé de détection d'objet solide Download PDF

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Publication number
WO2013121911A1
WO2013121911A1 PCT/JP2013/052476 JP2013052476W WO2013121911A1 WO 2013121911 A1 WO2013121911 A1 WO 2013121911A1 JP 2013052476 W JP2013052476 W JP 2013052476W WO 2013121911 A1 WO2013121911 A1 WO 2013121911A1
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Prior art keywords
dimensional object
image
vehicle
detected
object detection
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PCT/JP2013/052476
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English (en)
Japanese (ja)
Inventor
早川　泰久
修 深田
田中　慎也
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日産自動車株式会社
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Priority to JP2014500170A priority Critical patent/JP5794378B2/ja
Publication of WO2013121911A1 publication Critical patent/WO2013121911A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
    • B60R2300/30—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the type of image processing
    • B60R2300/302—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the type of image processing combining image information with GPS information or vehicle data, e.g. vehicle speed, gyro, steering angle data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
    • B60R2300/30—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the type of image processing
    • B60R2300/307—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the type of image processing virtually distinguishing relevant parts of a scene from the background of the scene
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
    • B60R2300/60—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by monitoring and displaying vehicle exterior scenes from a transformed perspective
    • B60R2300/607—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by monitoring and displaying vehicle exterior scenes from a transformed perspective from a bird's eye viewpoint
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
    • B60R2300/80—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the intended use of the viewing arrangement
    • B60R2300/8053—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the intended use of the viewing arrangement for bad weather conditions or night vision

Definitions

  • the present invention relates to a three-dimensional object detection device and a three-dimensional object detection method.
  • This application claims priority based on Japanese Patent Application No. 2012-031543 filed on Feb. 16, 2012.
  • the contents described in the application are incorporated into the present application by reference and made a part of the description of the present application.
  • the vehicle is detected based on the positional relationship between the vertical edge line segments and the positional relationship between the vertical edge line segment pair and the horizontal edge line segment.
  • a vehicle detection method is known (see Patent Document 1).
  • the accuracy of vehicle detection can be improved when the contrast of the entire image is poor due to bad weather, etc., but when local smear occurs in a part of the image such as light reflected on a puddle on the road surface
  • the accuracy of vehicle detection cannot be improved.
  • the problem to be solved by the present invention is when light is reflected on a puddle formed on the road surface and a local smear occurs in the image, or when an object other than the detection target is reflected in the puddle formed on the road surface. Even so, it is to provide a three-dimensional object detection device that detects other vehicles traveling in the detection region with high accuracy.
  • the present invention determines whether the detected three-dimensional object image is a virtual image or a real image based on the luminance of the image information, and determines that the three-dimensional object image is a virtual image.
  • the above-mentioned problem is solved by controlling the three-dimensional object to be suppressed from being determined to be another vehicle.
  • the image of the three-dimensional object detected in the predetermined detection area behind the host vehicle is a virtual image
  • the light may be reflected on the puddle formed on the road surface and the surrounding buildings may be reflected.
  • FIG. 1 is a schematic configuration diagram of a vehicle according to an embodiment to which a three-dimensional object detection device of the present invention is applied. It is a top view (three-dimensional object detection by difference waveform information) which shows the driving state of the vehicle of FIG. It is a block diagram which shows the detail of the computer of FIG. 4A and 4B are diagrams for explaining the outline of processing of the alignment unit in FIG. 3, in which FIG. 3A is a plan view showing a moving state of the vehicle, and FIG. It is the schematic which shows the mode of the production
  • FIG. 4 is a flowchart (No. 1) illustrating a three-dimensional object detection method using differential waveform information executed by the viewpoint conversion unit, the alignment unit, the smear detection unit, and the three-dimensional object detection unit of FIG. 3.
  • FIG. 1 is a flowchart (No. 1) illustrating a three-dimensional object detection method using differential waveform information executed by the viewpoint conversion unit, the alignment unit, the smear detection unit, and the three-dimensional object detection unit of FIG. 3.
  • FIG. 4 is a flowchart (part 2) illustrating a three-dimensional object detection method using differential waveform information executed by the viewpoint conversion unit, the alignment unit, the smear detection unit, and the three-dimensional object detection unit of FIG. 3. It is a figure (three-dimensional object detection by edge information) which shows the running state of vehicles of Drawing 1, (a) is a top view showing the positional relationship of a detection field etc., and (b) shows the positional relationship of a detection field etc. in real space. It is a perspective view shown. 4A and 4B are diagrams for explaining the operation of the luminance difference calculation unit in FIG. 3, in which FIG.
  • 3A is a diagram illustrating a positional relationship among attention lines, reference lines, attention points, and reference points in a bird's-eye view image;
  • FIG. It is a figure which shows the positional relationship of the attention line, reference line, attention point, and reference point.
  • 4A and 4B are diagrams for explaining the detailed operation of the luminance difference calculation unit in FIG. 3, in which FIG. 3A is a diagram illustrating a detection region in a bird's-eye view image, and FIG. It is a figure which shows the positional relationship of a reference point.
  • FIG. 4 is a flowchart (part 1) illustrating a three-dimensional object detection method using edge information executed by a viewpoint conversion unit, a luminance difference calculation unit, an edge line detection unit, and a three-dimensional object detection unit in FIG. 3;
  • FIG. 4 is a flowchart (part 1) illustrating a three-dimensional object detection method using edge information executed by a viewpoint conversion unit, a luminance difference calculation unit, an edge line detection unit, and a three-dimensional object detection unit in FIG. 3;
  • FIG. 4 is a flowchart (part 2) illustrating a three-dimensional object detection method using edge information executed by the viewpoint conversion unit, the luminance difference calculation unit, the edge line detection unit, and the three-dimensional object detection unit of FIG. 3. It is a figure which shows the example of an image for demonstrating edge detection operation
  • FIG. 1 is a schematic configuration diagram of a vehicle according to an embodiment to which a three-dimensional object detection device 1 of the present invention is applied.
  • the three-dimensional object detection device 1 of the present example is careful when the driver of the host vehicle V is driving. Is a device that detects, as an obstacle, other vehicles that are likely to be contacted, for example, other vehicles that may be contacted when the host vehicle V changes lanes.
  • the three-dimensional object detection device 1 of this example detects another vehicle that travels in an adjacent lane (hereinafter also simply referred to as an adjacent lane) adjacent to the lane in which the host vehicle travels. Further, the three-dimensional object detection device 1 of the present example can calculate the detected movement distance and movement speed of the other vehicle.
  • the three-dimensional object detection device 1 is mounted on the own vehicle V, and the three-dimensional object detected around the own vehicle travels in the adjacent lane next to the lane on which the own vehicle V travels.
  • An example of detecting a vehicle will be shown.
  • the three-dimensional object detection device 1 of this example includes a camera 10, a vehicle speed sensor 20, a calculator 30, and a steering angle sensor 0.
  • the camera 10 is attached to the host vehicle V so that the optical axis is at an angle ⁇ from the horizontal to the lower side at a height h at the rear of the host vehicle V.
  • the camera 10 images a predetermined area in the surrounding environment of the host vehicle V from this position.
  • the vehicle speed sensor 20 detects the traveling speed of the host vehicle V, and calculates the vehicle speed from the wheel speed detected by, for example, a wheel speed sensor that detects the rotational speed of the wheel.
  • the computer 30 detects a three-dimensional object behind the vehicle, and calculates a moving distance and a moving speed for the three-dimensional object in this example.
  • FIG. 2 is a plan view showing a traveling state of the host vehicle V in FIG.
  • the camera 10 images the vehicle rear side at a predetermined angle of view a.
  • the angle of view a of the camera 10 is set to an angle of view at which the left and right lanes can be imaged in addition to the lane in which the host vehicle V travels.
  • the area that can be imaged includes detection target areas A1 and A2 on the adjacent lane that is behind the host vehicle V and that is adjacent to the left and right of the travel lane of the host vehicle V.
  • FIG. 3 is a block diagram showing details of the computer 30 of FIG. In FIG. 3, the camera 10 and the vehicle speed sensor 20 are also shown in order to clarify the connection relationship.
  • the computer 30 includes a viewpoint conversion unit 31, an alignment unit 32, a three-dimensional object detection unit 33, a three-dimensional object determination unit 34, a virtual image determination unit 38, a control unit 39, and smear detection. Part 40.
  • the calculation unit 30 of the present embodiment has a configuration relating to a three-dimensional object detection block using differential waveform information.
  • the calculation unit 30 of the present embodiment can also be configured with respect to a three-dimensional object detection block using edge information. In this case, in the configuration shown in FIG. 3, the luminance difference calculation unit 35, the edge line detection unit 36, and the detection block configuration A configured by the alignment unit 32 and the three-dimensional object detection unit 33 are surrounded by a broken line.
  • the detection block configuration B including the three-dimensional object detection unit 37 can be replaced.
  • both of the detection block configuration A and the detection block configuration B are provided, and it is possible to detect a three-dimensional object using difference waveform information and to detect a three-dimensional object using edge information.
  • the detection block configuration A and the detection block configuration B are provided, either the detection block configuration A or the detection block configuration B can be operated according to environmental factors such as brightness.
  • the difference waveform information and the edge information in the present embodiment are an aspect of pixel distribution information indicating a predetermined luminance difference.
  • the “pixel distribution information” in the present embodiment is “pixels whose luminance difference is equal to or greater than a predetermined threshold” detected along the direction in which the three-dimensional object falls when the captured image is converted into a bird's-eye view image. It is information which shows the state of distribution. That is, in the three-dimensional object detection unit 33 in the detection block configuration A and the three-dimensional object detection unit 37 in the detection block configuration B, in the bird's-eye view image obtained by the image conversion unit, the three-dimensional object collapses when the viewpoint is converted to the bird's-eye view image. A three-dimensional object is detected based on distribution information of pixels whose luminance difference is greater than or equal to a predetermined threshold along the direction. Each configuration will be described below.
  • the three-dimensional object detection device 1 detects a three-dimensional object existing in the right detection area or the left detection area behind the vehicle based on image information obtained by the monocular camera 1 that images the rear of the vehicle.
  • the viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging with the camera 10, and converts the viewpoint of the input captured image data into bird's-eye image data in a bird's-eye view state.
  • the state viewed from a bird's-eye view is a state viewed from the viewpoint of a virtual camera looking down from above, for example, vertically downward.
  • This viewpoint conversion can be executed as described in, for example, Japanese Patent Application Laid-Open No. 2008-219063.
  • the viewpoint conversion of captured image data to bird's-eye view image data is based on the principle that a vertical edge peculiar to a three-dimensional object is converted into a straight line group passing through a specific fixed point by viewpoint conversion to bird's-eye view image data. This is because a planar object and a three-dimensional object can be distinguished if used. Note that the result of the image conversion processing by the viewpoint conversion unit 31 is also used in detection of a three-dimensional object by edge information described later.
  • the alignment unit 32 sequentially inputs the bird's-eye image data obtained by the viewpoint conversion of the viewpoint conversion unit 31, and aligns the positions of the inputted bird's-eye image data at different times.
  • 4A and 4B are diagrams for explaining the outline of the processing of the alignment unit 32, where FIG. 4A is a plan view showing the moving state of the host vehicle V, and FIG. 4B is an image showing the outline of the alignment.
  • the host vehicle V at the current time is located at V1, and the host vehicle V one hour before is located at V2.
  • the other vehicle VX is located in the rear direction of the own vehicle V and is in parallel with the own vehicle V, the other vehicle VX at the current time is located at V3, and the other vehicle VX one hour before is located at V4.
  • the host vehicle V has moved a distance d at one time.
  • “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.
  • the bird's-eye image PB t at the current time is as shown in Figure 4 (b).
  • the bird's-eye image PB t becomes a rectangular shape for the white line drawn on the road surface, but a relatively accurate is a plan view state, tilting occurs about the position of another vehicle VX at position V3.
  • the white line drawn on the road surface has a rectangular shape and is relatively accurately viewed in plan, but the other vehicle VX at the position V4 Falls down.
  • the vertical edges of solid objects are straight lines along the collapse direction by the viewpoint conversion processing to bird's-eye view image data. This is because the plane image on the road surface does not include a vertical edge, but such a fall does not occur even when the viewpoint is changed.
  • the alignment unit 32 performs alignment of the bird's-eye images PB t and PB t ⁇ 1 as described above on the data. At this time, the alignment unit 32 is offset a bird's-eye view image PB t-1 before one unit time, to match the position and bird's-eye view image PB t at the current time.
  • the image on the left side and the center image in FIG. 4B show a state that is offset by the movement distance d ′.
  • This offset amount d ′ is a movement amount on the bird's-eye view image data corresponding to the actual movement distance d of the host vehicle V shown in FIG. It is determined based on the time until the time.
  • the alignment unit 32 takes the difference between the bird's-eye images PB t and PB t ⁇ 1 and generates data of the difference image PD t .
  • the pixel value of the difference image PD t may be an absolute value of the difference between the pixel values of the bird's-eye images PB t and PB t ⁇ 1 , and the absolute value is predetermined in order to cope with a change in the illuminance environment. It may be set to “1” when the threshold value p is exceeded and “0” when the threshold value p is not exceeded.
  • the image on the right side of FIG. 4B is the difference image PD t .
  • This threshold value p may be set in advance, or may be changed according to a control command corresponding to a result of virtual image determination by the control unit 39 described later.
  • the three-dimensional object detection unit 33 detects a three-dimensional object based on the data of the difference image PD t shown in FIG. At this time, the three-dimensional object detection unit 33 of this example also calculates the movement distance of the three-dimensional object in the real space. In detecting the three-dimensional object and calculating the movement distance, the three-dimensional object detection unit 33 first generates a differential waveform. Note that the moving distance of the three-dimensional object per time is used for calculating the moving speed of the three-dimensional object. The moving speed of the three-dimensional object can be used to determine whether or not the three-dimensional object is a vehicle.
  • Three-dimensional object detection unit 33 of the present embodiment when generating the differential waveform sets a detection area in the difference image PD t.
  • the three-dimensional object detection device 1 of the present example is another vehicle that the driver of the host vehicle V pays attention to, in particular, the lane in which the host vehicle V that may be contacted when the host vehicle V changes lanes travels. Another vehicle traveling in the adjacent lane is detected as a detection target. For this reason, in this example which detects a solid object based on image information, two detection areas are set on the right side and the left side of the host vehicle V in the image obtained by the camera 1. Specifically, in the present embodiment, rectangular detection areas A1 and A2 are set on the left and right sides behind the host vehicle V as shown in FIG.
  • the other vehicle detected in the detection areas A1 and A2 is detected as an obstacle traveling in the adjacent lane adjacent to the lane in which the host vehicle V is traveling.
  • Such detection areas A1 and A2 may be set from a relative position with respect to the host vehicle V, or may be set based on the position of the white line.
  • the movement distance detection device 1 may use, for example, an existing white line recognition technique.
  • the three-dimensional object detection unit 33 recognizes the sides (sides along the traveling direction) of the set detection areas A1 and A2 on the own vehicle V side as the ground lines L1 and L2 (FIG. 2).
  • the ground line means a line in which the three-dimensional object contacts the ground.
  • the ground line is set as described above, not a line in contact with the ground. Even in this case, from experience, the difference between the ground line according to the present embodiment and the ground line obtained from the position of the other vehicle VX is not too large, and there is no problem in practical use.
  • FIG. 5 is a schematic diagram illustrating how a differential waveform is generated by the three-dimensional object detection unit 33 illustrated in FIG. 3.
  • the three-dimensional object detection unit 33 calculates a differential waveform from a portion corresponding to the detection areas A ⁇ b> 1 and A ⁇ b> 2 in the difference image PD t (right diagram in FIG. 4B) calculated by the alignment unit 32.
  • DW t is generated.
  • the three-dimensional object detection unit 33 generates a differential waveform DW t along the direction in which the three-dimensional object falls by viewpoint conversion.
  • the difference waveform DW t is generated for the detection area A2 in the same procedure.
  • the three-dimensional object detection unit 33 defines a line La in the direction in which the three-dimensional object falls on the data of the difference image DW t . Then, the three-dimensional object detection unit 33 counts the number of difference pixels DP indicating a predetermined difference on the line La.
  • the difference pixel DP indicating the predetermined difference has a predetermined threshold value when the pixel value of the difference image DW t is an absolute value of the difference between the pixel values of the bird's-eye images PB t and PB t ⁇ 1. If the pixel value of the difference image DW t is expressed as “0” or “1”, the pixel indicates “1”.
  • the three-dimensional object detection unit 33 counts the number of difference pixels DP and then obtains an intersection point CP between the line La and the ground line L1. Then, the three-dimensional object detection unit 33 associates the intersection CP with the count number, determines the horizontal axis position based on the position of the intersection CP, that is, the position on the vertical axis in the right diagram of FIG. The axis position, that is, the position on the right and left axis in the right diagram of FIG.
  • the three-dimensional object detection unit 33 defines lines Lb, Lc... In the direction in which the three-dimensional object falls, counts the number of difference pixels DP, and determines the horizontal axis position based on the position of each intersection CP. Then, the vertical axis position is determined from the count number (number of difference pixels DP) and plotted.
  • the three-dimensional object detection unit 33 generates the differential waveform DW t as shown in the right diagram of FIG.
  • the line La and the line Lb in the direction in which the three-dimensional object collapses have different distances overlapping the detection area A1. For this reason, if the detection area A1 is filled with the difference pixels DP, the number of difference pixels DP is larger on the line La than on the line Lb. For this reason, when the three-dimensional object detection unit 33 determines the vertical axis position from the count number of the difference pixels DP, the three-dimensional object detection unit 33 is normalized based on the distance at which the lines La and Lb in the direction in which the three-dimensional object falls and the detection area A1 overlap. Turn into. As a specific example, in the left diagram of FIG.
  • the three-dimensional object detection unit 33 normalizes the count number by dividing it by the overlap distance.
  • 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 33 calculates the movement distance by comparison with the differential waveform DW t ⁇ 1 one time before. That is, the three-dimensional object detection unit 33 calculates the movement distance from the time change of the difference waveforms DW t and DW t ⁇ 1 .
  • the three-dimensional object detection unit 33 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).
  • FIG. 6 is a diagram illustrating the small areas DW t1 to DW tn divided by the three-dimensional object detection unit 33.
  • the small 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.
  • the three-dimensional object detection unit 33 obtains an offset amount (amount of movement of the differential waveform in the horizontal axis direction (vertical direction in FIG. 6)) for each of the small areas DW t1 to DW tn .
  • 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).
  • three-dimensional object detection unit 33 for each small area DW t1 ⁇ DW tn, when moving the differential waveform DW t1 before one unit time in the horizontal axis direction, the differential waveform DW t at the current time The position where the error is minimized (the position in the horizontal axis direction) is determined, and the amount of movement in the horizontal axis between the original position of the differential waveform DW t ⁇ 1 and the position where the error is minimized is obtained as an offset amount. Then, the three-dimensional object detection unit 33 counts the offset amount obtained for each of the small areas DW t1 to DW tn and forms a histogram.
  • the differential waveform information of the present embodiment can be positioned as “distribution information of pixels having a luminance difference equal to or greater than a predetermined threshold along the direction in which the three-dimensional object falls when the viewpoint is converted into a bird's-eye view image”.
  • FIG. 7 is a diagram illustrating an example of a histogram obtained by the three-dimensional object detection unit 33.
  • the offset amount which is the amount of movement that minimizes the error between each of the small areas DW t1 to DW tn and the differential waveform DW t ⁇ 1 one time before, has some variation.
  • the three-dimensional object detection unit 33 forms a histogram of offset amounts including variations, and calculates a movement distance from the histogram.
  • the three-dimensional object detection unit 33 calculates the moving distance of the three-dimensional object from the maximum value of the histogram. That is, in the example illustrated in FIG.
  • the three-dimensional object detection unit 33 calculates the offset amount indicating the maximum value of the histogram as the movement distance ⁇ * .
  • the moving distance ⁇ * is a relative moving distance of the other vehicle VX with respect to the host vehicle V. For this reason, when calculating the absolute movement distance, the three-dimensional object detection unit 33 calculates the absolute movement distance based on the obtained movement distance ⁇ * and the signal from the vehicle speed sensor 20.
  • the three-dimensional object detection unit 33 weights each of the plurality of small areas DW t1 to DW tn and forms a histogram by counting the offset amount obtained for each of the small areas DW t1 to DW tn according to the weight. May be.
  • FIG. 8 is a diagram illustrating weighting by the three-dimensional object detection unit 33.
  • 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. Three-dimensional object detection unit 33 to reduce the weight for such small area DW m. This is because the flat small area DW m has no characteristics and is likely to have a large error in calculating the offset amount.
  • the small region DW m + k (k is an integer equal to or less than nm) is rich in undulations. That is, in the small area DW m , the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference is large.
  • Three-dimensional object detection unit 33 increases the weight for such small area DW m. This is because the small region DW m + k rich in undulations is characteristic and there is a high possibility that the offset amount can be accurately calculated. By weighting in this way, the calculation accuracy of the movement distance can be improved.
  • the differential waveform DW t is divided into a plurality of small areas DW t1 to DW tn in order to improve the calculation accuracy of the movement distance.
  • the small area DW t1 is divided. It is not necessary to divide into ⁇ DW tn .
  • the three-dimensional object detection unit 33 calculates the moving distance from the offset amount of the differential waveform DW t when the error between the differential waveform DW t and the differential waveform DW t ⁇ 1 is minimized. That is, the method for obtaining the offset amount of the difference waveform DW t in the difference waveform DW t-1 and the current time before one unit time is not limited to the above disclosure.
  • the computer 30 includes a smear detection unit 40.
  • the smear detection unit 40 detects a smear generation region from data of a captured image obtained by imaging with the camera 10. Since smear is a whiteout phenomenon that occurs in a CCD image sensor or the like, the smear detection unit 40 may be omitted when the camera 10 using a CMOS image sensor or the like that does not generate such smear is employed.
  • FIG. 9 is an image diagram for explaining the processing by the smear detection unit 40 and the calculation processing of the differential waveform DW t thereby.
  • data of the captured image P in which the smear S exists is input to the smear detection unit 40.
  • the smear detection unit 40 detects the smear S from the captured image P.
  • There are various methods for detecting the smear S For example, in the case of a general CCD (Charge-Coupled Device) camera, the smear S is generated only in the downward direction of the image from the light source.
  • CCD Charge-Coupled Device
  • a region having a luminance value equal to or higher than a predetermined value from the lower side of the image to the upper side of the image and continuous in the vertical direction is searched, and this is identified as a smear S generation region.
  • the smear detection unit 40 generates smear image SP data in which the pixel value is set to “1” for the place where the smear S occurs and the other place is set to “0”. After the generation, the smear detection unit 40 transmits the data of the smear image SP to the viewpoint conversion unit 31.
  • the viewpoint conversion unit 31 to which the data of the smear image SP is input converts the viewpoint into a state of bird's-eye view.
  • the viewpoint conversion unit 31 generates data of the smear bird's-eye view image SB t .
  • the viewpoint conversion unit 31 transmits the data of the smear bird's-eye view image SB t to the alignment unit 33.
  • the viewpoint conversion unit 31 transmits the data of the smear bird's-eye view image SB t ⁇ 1 one hour before to the alignment unit 33.
  • the alignment unit 32 aligns the smear bird's-eye images SB t and SB t ⁇ 1 on the data.
  • the specific alignment is the same as the case where the alignment of the bird's-eye images PB t and PB t ⁇ 1 is executed on the data.
  • the alignment unit 32 performs a logical sum on the smear S generation region of each smear bird's-eye view image SB t , SB t ⁇ 1 . Thereby, the alignment part 32 produces
  • the alignment unit 32 transmits the data of the mask image MP to the three-dimensional object detection unit 33.
  • the three-dimensional object detection unit 33 sets the count number of the frequency distribution to zero for the portion corresponding to the smear S generation region in the mask image MP. That is, when the differential waveform DW t as shown in FIG. 9 is generated, the three-dimensional object detection unit 33 sets the count number SC by the smear S to zero and generates a corrected differential waveform DW t ′. Become.
  • the three-dimensional object detection unit 33 obtains the moving speed of the vehicle V (camera 10), and obtains the offset amount for the stationary object from the obtained moving speed. After obtaining the offset amount of the stationary object, the three-dimensional object detection unit 33 calculates the moving distance of the three-dimensional object after ignoring the offset amount corresponding to the stationary object among the maximum values of the histogram.
  • FIG. 10 is a diagram illustrating another example of a histogram obtained by the three-dimensional object detection unit 33.
  • a stationary object exists in addition to the other vehicle VX within the angle of view of the camera 10, two maximum values ⁇ 1 and ⁇ 2 appear in the obtained histogram.
  • one of the two maximum values ⁇ 1, ⁇ 2 is the offset amount of the stationary object.
  • the three-dimensional object detection unit 33 calculates the offset amount for the stationary object from the moving speed, ignores the maximum value corresponding to the offset amount, and calculates the moving distance of the three-dimensional object by using the remaining maximum value. To do.
  • the three-dimensional object detection unit 33 stops calculating the movement distance.
  • 11 and 12 are flowcharts showing the three-dimensional object detection procedure of this embodiment.
  • the computer 30 inputs data of the image P captured by the camera 10, and generates a smear image SP by the smear detector 40 (S1).
  • the viewpoint conversion unit 31 generates data of the bird's-eye view image PB t from the data of the captured image P from the camera 10, and also generates data of the smear bird's-eye view image SB t from the data of the smear image SP (S2).
  • the alignment unit 33 aligns the data of the bird's-eye view image PB t and the data of the bird's-eye view image PB t ⁇ 1 one hour ago, and the data of the smear bird's-eye image SB t and the smear bird's-eye view one hour ago.
  • the data of the image SB t-1 is aligned (S3).
  • the alignment unit 33 generates data for the difference image PD t and also generates data for the mask image MP (S4).
  • three-dimensional object detection unit 33, the data of the difference image PD t, and a one unit time before the difference image PD t-1 of the data generates a difference waveform DW t (S5).
  • the three-dimensional object detection unit 33 After generating the differential waveform DW t , the three-dimensional object detection unit 33 sets the count number corresponding to the generation area of the smear S in the differential waveform DW t to zero, and suppresses the influence of the smear S (S6).
  • the three-dimensional object detection unit 33 determines whether or not the peak of the differential waveform DW t is greater than or equal to the first threshold value ⁇ (S7).
  • the first threshold value ⁇ can be set in advance and can be changed according to the control command of the control unit 39 shown in FIG. 3, and details thereof will be described later.
  • the peak of the difference waveform DW t is not equal to or greater than the first threshold value ⁇ , that is, when there is almost no difference, it is considered that there is no three-dimensional object in the captured image P.
  • the three-dimensional object detection unit 33 does not have a three-dimensional object and has another vehicle as an obstacle. It is determined not to do so (FIG. 12: S16). Then, the processes shown in FIGS. 11 and 12 are terminated.
  • the three-dimensional object detection unit 33 determines that a three-dimensional object exists, and sets the difference waveform DW t to a plurality of difference waveforms DW t .
  • the area is divided into small areas DW t1 to DW tn (S8).
  • the three-dimensional object detection unit 33 performs weighting for each of the small areas DW t1 to DW tn (S9).
  • the three-dimensional object detection unit 33 calculates an offset amount for each of the small areas DW t1 to DW tn (S10), and generates a histogram with weights added (S11).
  • the three-dimensional object detection unit 33 calculates a relative movement distance that is a movement distance of the three-dimensional object with respect to the host vehicle V based on the histogram (S12). Next, the three-dimensional object detection unit 33 calculates the absolute movement speed of the three-dimensional object from the relative movement distance (S13). At this time, the three-dimensional object detection unit 33 calculates the relative movement speed by differentiating the relative movement distance with respect to time, and adds the own vehicle speed detected by the vehicle speed sensor 20 to calculate the absolute movement speed.
  • the three-dimensional object detection unit 33 determines whether the absolute movement speed of the three-dimensional object is 10 km / h or more and the relative movement speed of the three-dimensional object with respect to the host vehicle V is +60 km / h or less (S14). When both are satisfied (S14: YES), the three-dimensional object detection unit 33 determines that the three-dimensional object is the other vehicle VX (S15). Then, the processes shown in FIGS. 11 and 12 are terminated. On the other hand, when either one is not satisfied (S14: NO), the three-dimensional object detection unit 33 determines that there is no other vehicle (S16). Then, the processes shown in FIGS. 11 and 12 are terminated.
  • the rear side of the host vehicle V is set as the detection areas A1 and A2, and the vehicle V travels in the adjacent lane that travels next to the travel lane of the host vehicle to which attention should be paid while traveling.
  • Emphasis is placed on detecting the vehicle VX, and in particular, whether or not there is a possibility of contact when the host vehicle V changes lanes. This is to determine whether or not there is a possibility of contact with another vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the own vehicle when the own vehicle V changes lanes. For this reason, the process of step S14 is performed.
  • step S14 it is determined whether the absolute moving speed of the three-dimensional object is 10 km / h or more and the relative moving speed of the three-dimensional object with respect to the vehicle V is +60 km / h or less.
  • the absolute moving speed of the stationary object may be detected to be several km / h. Therefore, by determining whether the speed is 10 km / h or more, it is possible to reduce the possibility of determining that the stationary object is the other vehicle VX.
  • the relative speed of the three-dimensional object with respect to the host vehicle V may be detected at a speed exceeding +60 km / h. Therefore, the possibility of erroneous detection due to noise can be reduced by determining whether the relative speed is +60 km / h or less.
  • the threshold of the relative movement speed for determining the other vehicle VX in step S14 can be arbitrarily set. For example, -20 km / h or more and 100 km / h or less can be set as the relative movement speed threshold.
  • the negative lower limit value is a lower limit value of the moving speed when the detected object moves rearward of the host vehicle VX, that is, when the detected object flows backward.
  • This threshold value can be set in advance as appropriate, but can be changed in accordance with a control command of the control unit 39 described later.
  • step S14 it may be determined that the absolute movement speed is not negative or not 0 km / h. Further, in the present embodiment, since emphasis is placed on whether or not there is a possibility of contact when the host vehicle V changes lanes, when another vehicle VX is detected in step S15, the driver of the host vehicle is notified. A warning sound may be emitted or a display corresponding to a warning may be performed by a predetermined display device.
  • the number of pixels indicating a predetermined difference is counted on the data of the difference image PD t along the direction in which the three-dimensional object falls by viewpoint conversion.
  • the difference waveform DW t is generated by frequency distribution.
  • the pixel indicating the predetermined difference on the data of the difference image PD t is a pixel that has changed in an image at a different time, in other words, a place where a three-dimensional object exists.
  • 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.
  • the differential waveform DW t is generated from the information in the height direction for the three-dimensional object. Then, the moving distance of the three-dimensional object is calculated from the time change of the differential waveform DW t including the information in the height direction. For this reason, compared with the case where only one point of movement is focused on, the detection location before the time change and the detection location after the time change are specified including information in the height direction. The same location is likely to be obtained, and the movement distance is calculated from the time change of the same location, so that the calculation accuracy of the movement distance can be improved.
  • the count number of the frequency distribution is set to zero for the portion corresponding to the smear S generation region in the differential waveform DW t .
  • the waveform portion generated by the smear S in the differential waveform DW t is removed, and a situation in which the smear S is mistaken as a three-dimensional object can be prevented.
  • the moving distance of the three-dimensional object is calculated from the offset amount of the differential waveform DW t when the error of the differential waveform DW t generated at different times is minimized. For this reason, the movement distance is calculated from the offset amount of the one-dimensional information called the waveform, and the calculation cost can be suppressed in calculating the movement distance.
  • the differential waveform DW t generated at different times is divided into a plurality of small regions DW t1 to DW tn .
  • a plurality of waveforms representing respective portions of the three-dimensional object are obtained.
  • weighting is performed for each of the plurality of small areas DW t1 to DW tn , and the offset amount obtained for each of the small areas DW t1 to DW tn is counted according to the weight to form a histogram. For this reason, the moving distance can be calculated more appropriately by increasing the weight for the characteristic area and decreasing the weight for the non-characteristic area. Therefore, the calculation accuracy of the moving distance can be further improved.
  • the weight is increased as the difference between the maximum value and the minimum value of the number of pixels indicating a predetermined difference increases. For this reason, the characteristic undulation region having a large difference between the maximum value and the minimum value has a larger weight, and the flat region having a small undulation has a smaller weight.
  • the moving distance is calculated by increasing the weight in the area where the difference between the maximum value and the minimum value is large. The accuracy can be further improved.
  • the moving distance of the three-dimensional object is calculated from the maximum value of the histogram obtained by counting the offset amount obtained for each of the small areas DW t1 to DW tn . For this reason, even if there is a variation in the offset amount, a more accurate movement distance can be calculated from the maximum value.
  • the offset amount for a stationary object is obtained and this offset amount is ignored, it is possible to prevent a situation in which the calculation accuracy of the moving distance of the three-dimensional object is lowered due to the stationary object.
  • the calculation of the moving distance of the three-dimensional object is stopped. For this reason, it is possible to prevent a situation in which an erroneous movement distance having a plurality of maximum values is calculated.
  • the speed of the vehicle V is determined based on a signal from the vehicle speed sensor 20 is not limited thereto, it may be estimated velocity from a plurality of images of different times. In this case, a vehicle speed sensor becomes unnecessary, and the configuration can be simplified.
  • the captured image at the current time and the image one hour before are converted into a bird's-eye view, the converted bird's-eye view is aligned, the difference image PD t is generated, and the generated difference image PD
  • t is evaluated along the falling direction (the falling direction of the three-dimensional object when the captured image is converted into a bird's eye view)
  • the differential waveform DW t is generated, but the present invention is not limited to this.
  • the differential waveform DW t may be generated by evaluating along the direction corresponding to the falling direction (that is, the direction in which the falling direction is converted into the direction on the captured image).
  • the difference image PD t is generated from the difference between the two images subjected to the alignment, and the difference image PD t is converted into a bird's eye view
  • the bird's-eye view does not necessarily have to be clearly generated as long as the evaluation can be performed along the direction in which the user falls.
  • FIGS. 13A and 13B are diagrams illustrating an imaging range and the like of the camera 10 in FIG. 3.
  • FIG. 13A is a plan view
  • FIG. 13B is a perspective view in real space on the rear side from the host vehicle V. Show.
  • the camera 10 has a predetermined angle of view a, and images the rear side from the host vehicle V included in the predetermined angle of view a.
  • the angle of view “a” of the camera 10 is set so that the imaging range of the camera 10 includes the adjacent lane in addition to the lane in which the host vehicle V travels.
  • the detection areas A1 and A2 in this example are trapezoidal in a plan view (when viewed from a bird's eye view), and the positions, sizes, and shapes of the detection areas A1 and A2 are determined based on the distances d 1 to d 4. 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.
  • the distance d1 is a distance from the host vehicle V to the ground lines L1 and L2.
  • the ground lines L1 and L2 mean lines on which a three-dimensional object existing in the lane adjacent to the lane in which the host vehicle V travels contacts the ground.
  • a distance d1 which is a position to be the ground lines L1 and L2 of the other vehicle VX is obtained from a distance d11 from the own vehicle V to the white line W and a distance d12 from the white line W to a position where the other vehicle VX is predicted to travel. It can be determined substantially fixedly.
  • the distance d1 is not limited to being fixedly determined, and may be variable.
  • the computer 30 recognizes the position of the white line W with respect to the host vehicle V by a technique such as white line recognition, and determines the distance d11 based on the recognized position of the white line W.
  • the distance d1 is variably set using the determined distance d11.
  • the distance d1 is It shall be fixedly determined.
  • the distance d2 is a distance extending from the rear end portion of the host vehicle V in the vehicle traveling direction.
  • the distance d2 is determined so that the detection areas A1 and A2 are at least within the angle of view a of the camera 10.
  • the distance d2 is set so as to be in contact with the range divided into the angle of view a.
  • the distance d3 is a distance indicating the length of the detection areas A1, A2 in the vehicle traveling direction. This distance d3 is determined based on the size of the three-dimensional object to be detected. In the present embodiment, since the detection target is the other vehicle VX or the like, the distance d3 is set to a length including the other vehicle VX.
  • the distance d4 is a distance indicating a height set so as to include a tire such as the other vehicle VX in the real space.
  • the distance d4 is a length shown in FIG. 13A in the bird's-eye view image.
  • the distance d4 may be a length that does not include a lane that is further adjacent to the left and right adjacent lanes in the bird's-eye view image (that is, a lane that is adjacent to two lanes).
  • 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.
  • 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.
  • the areas surrounded by the sides b1 to b4 are set as the detection areas A1 and A2.
  • the detection areas A ⁇ b> 1 and A ⁇ b> 2 are true squares (rectangles) in the real space behind the host vehicle V.
  • the viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging with the camera 10.
  • the viewpoint conversion unit 31 performs viewpoint conversion processing on the input captured image data to the bird's-eye image data in a bird's-eye view state.
  • the bird's-eye view is a state seen from the viewpoint of a virtual camera looking down from above, for example, vertically downward (or slightly obliquely downward).
  • This viewpoint conversion process can be realized by a technique described in, for example, Japanese Patent Application Laid-Open No. 2008-219063.
  • Luminance difference calculation unit 35 to detect edges of the three-dimensional object included in the bird's-eye view image, with respect to the bird's-eye view image data viewpoint converted by the viewpoint conversion unit 31, calculates the brightness difference. For each of a plurality of positions along a vertical imaginary line extending in the vertical direction in the real space, the brightness difference calculating unit 35 calculates a brightness difference between two pixels in the vicinity of each position.
  • the luminance difference calculation unit 35 can calculate the luminance difference by either a method of setting only one vertical virtual line extending in the vertical direction in the real space or a method of setting two vertical virtual lines.
  • the brightness difference calculation unit 35 applies a first vertical imaginary line corresponding to a line segment extending in the vertical direction in the real space and a vertical direction in the real space different from the first vertical imaginary line with respect to the bird's-eye view image that has undergone viewpoint conversion.
  • a second vertical imaginary line corresponding to the extending line segment is set.
  • the luminance difference calculation unit 35 continuously obtains a luminance difference between a point on the first vertical imaginary line and a point on the second vertical imaginary line along the first vertical imaginary line and the second vertical imaginary line.
  • the operation of the luminance difference calculation unit 35 will be described in detail.
  • the luminance difference calculation unit 35 corresponds to a line segment extending in the vertical direction in the real space and passes through the detection area A1 (hereinafter referred to as the attention line La).
  • the luminance difference calculation unit 35 corresponds to a line segment extending in the vertical direction in the real space and also passes through the second vertical virtual line Lr (hereinafter referred to as a reference line Lr) passing through the detection area A1.
  • the reference line Lr is set at a position separated from the attention line La by a predetermined distance in the real space.
  • the line corresponding to the line segment extending in the vertical direction in the real space is a line that spreads radially from the position Ps of the camera 10 in the bird's-eye view image.
  • This radially extending line is a line along the direction in which the three-dimensional object falls when converted to bird's-eye view.
  • the luminance difference calculation unit 35 sets the attention point Pa (point on the first vertical imaginary line) on the attention line La.
  • the luminance difference calculation unit 35 sets a reference point Pr (a point on the second vertical plate) on the reference line Lr.
  • the attention line La, the attention point Pa, the reference line Lr, and the reference point Pr have the relationship shown in FIG. 14B in the real space.
  • the attention line La and the reference line Lr are lines extending in the vertical direction in the real space, and the attention point Pa and the reference point Pr are substantially the same height in the real space. This is the point that is set.
  • the attention point Pa and the reference point Pr do not necessarily have the same height, and an error that allows the attention point Pa and the reference point Pr to be regarded as the same height is allowed.
  • the luminance difference calculation unit 35 obtains a luminance difference between the attention point Pa and the reference point Pr. If the luminance difference between the attention point Pa and the reference point Pr is large, it is considered that an edge exists between the attention point Pa and the reference point Pr. Therefore, the edge line detection unit 36 shown in FIG. 3 detects an edge line based on the luminance difference between the attention point Pa and the reference point Pr.
  • FIG. 15 is a diagram illustrating a detailed operation of the luminance difference calculation unit 35, in which FIG. 15 (a) shows a bird's-eye view image in a bird's-eye view state, and FIG. 15 (b) is shown in FIG. 15 (a). It is the figure which expanded a part B1 of the bird's-eye view image. Although only the detection area A1 is illustrated and described in FIG. 15, the luminance difference is calculated in the same procedure for the detection area A2.
  • the other vehicle VX When the other vehicle VX is reflected in the captured image captured by the camera 10, the other vehicle VX appears in the detection area A1 in the bird's-eye view image as shown in FIG. As shown in the enlarged view of the area B1 in FIG. 15A in FIG. 15B, it is assumed that the attention line La is set on the rubber part of the tire of the other vehicle VX on the bird's-eye view image.
  • the luminance difference calculation unit 35 first sets the reference line Lr.
  • the reference line Lr is set along the vertical direction at a position away from the attention line La by a predetermined distance in the real space.
  • the reference line Lr is set at a position separated from the attention line La by 10 cm in real space.
  • the reference line Lr is set on the wheel of the tire of the other vehicle VX that is separated from the rubber of the tire of the other vehicle VX by, for example, 10 cm on the bird's eye view image.
  • the luminance difference calculation unit 35 sets a plurality of attention points Pa1 to PaN on the attention line La.
  • attention point Pai when an arbitrary point is indicated
  • the number of attention points Pa set on the attention line La may be arbitrary.
  • N attention points Pa are set on the attention line La.
  • the luminance difference calculation unit 35 sets the reference points Pr1 to PrN so as to be the same height as the attention points Pa1 to PaN in the real space. Then, the luminance difference calculation unit 35 calculates the luminance difference between the attention point Pa and the reference point Pr having the same height. Thereby, the luminance difference calculation unit 35 calculates the luminance difference between the two pixels for each of a plurality of positions (1 to N) along the vertical imaginary line extending in the vertical direction in the real space. For example, the luminance difference calculating unit 35 calculates a luminance difference between the first attention point Pa1 and the first reference point Pr1, and the second difference between the second attention point Pa2 and the second reference point Pr2. Will be calculated.
  • the luminance difference calculation unit 35 continuously calculates the luminance difference along the attention line La and the reference line Lr. That is, the luminance difference calculation unit 35 sequentially obtains the luminance difference between the third to Nth attention points Pa3 to PaN and the third to Nth reference points Pr3 to PrN.
  • the luminance difference calculation unit 35 repeatedly executes the above-described processing such as setting the reference line Lr, setting the attention point Pa and the reference point Pr, and calculating the luminance difference while shifting the attention line La in the detection area A1. That is, the luminance difference calculation unit 35 repeatedly executes the above processing while changing the positions of the attention line La and the reference line Lr by the same distance in the extending direction of the ground line L1 in the real space.
  • Luminance difference calculation unit 35 for example, to set the line which has been a reference line Lr in the previous process to attention line La, and set the reference line Lr to this attention line La, will seek sequential luminance difference It will be.
  • the edge line detection unit 36 detects an edge line from the continuous luminance difference calculated by the luminance difference calculation unit 35.
  • the first attention point Pa ⁇ b> 1 and the first reference point Pr ⁇ b> 1 are located in the same tire portion, and thus the luminance difference is small.
  • 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 becomes large. Therefore, the edge line detection unit 36 may detect that an edge line exists between the second to sixth attention points Pa2 to Pa6 and the second to sixth reference points Pr2 to Pr6 having a large luminance difference. it can.
  • the edge line detection unit 36 firstly follows the following Equation 1 to determine the i-th attention point Pai (coordinate (xi, yi)) and the i-th reference point Pri (coordinate ( xi ′, yi ′)) and the i th attention point Pai are attributed.
  • Equation 1 t represents a threshold value
  • I (xi, yi) represents the luminance value of the i-th attention point Pai
  • I (xi ′, yi ′) represents the luminance value of the i-th reference point Pri.
  • the attribute s (xi, yi) of the attention point Pai is “1”.
  • the brightness value of the attention point Pai is, when the reference point Pri lower than the luminance value obtained by subtracting the threshold t, the attribute s (xi, yi) of the attention point Pai becomes '-1'.
  • the threshold value t can be set in advance and can be changed in accordance with a control command issued by the control unit 39 shown in FIG. 3, and details thereof will be described later.
  • the edge line detection unit 36 determines whether or not the attention line La is an edge line from the continuity c (xi, yi) of the attribute s along the attention line La based on Equation 2 below.
  • the continuity c (xi, yi) is “1”.
  • 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”.
  • the edge line detection unit 36 obtains the sum for the continuity c of all the points of interest Pa on the line of interest La.
  • the edge line detection unit 36 normalizes the continuity c by dividing the obtained sum of continuity c by the number N of points of interest Pa.
  • the edge line detection unit 36 determines that the attention line La is an edge line when the normalized value exceeds the threshold ⁇ .
  • the threshold value ⁇ is a value set in advance through experiments or the like.
  • the threshold value ⁇ may be set in advance, or may be changed according to a control command corresponding to a virtual image determination result of the control unit 39 described later.
  • the edge line detection unit 36 determines whether or not the attention line La is an edge line based on Equation 3 below. Then, the edge line detection unit 36 determines whether or not all the attention lines La drawn on the detection area A1 are edge lines. [Equation 3] ⁇ c (xi, yi) / N> ⁇
  • the three-dimensional object detection unit 37 detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 36.
  • the three-dimensional object detection device 1 detects an edge line extending in the vertical direction in real space. The fact that many edge lines extending in the vertical direction are detected means that there is a high possibility that a three-dimensional object exists in the detection areas A1 and A2. For this reason, the three-dimensional object detection unit 37 detects a three-dimensional object based on the amount of edge lines detected by the edge line detection unit 36. Furthermore, prior to detecting the three-dimensional object, the three-dimensional object detection unit 37 determines whether or not the edge line detected by the edge line detection unit 36 is correct.
  • the three-dimensional object detection unit 37 determines whether or not the luminance change along the edge line of the bird's-eye view image on the edge line is larger than a predetermined threshold value. When the luminance change of the bird's-eye view image on the edge line is larger than the threshold value, it is determined that the edge line is detected by erroneous determination. On the other hand, when the luminance change of the bird's-eye view image on the edge line is not larger than the threshold value, it is determined that the edge line is correct.
  • This threshold value is a value set in advance by experiments or the like.
  • FIG. 16 is a diagram showing a luminance distribution of the edge line
  • FIG. 16 (a) shows the edge line and the luminance distribution in the case where the other vehicle VX as three-dimensional object was present in the detection area A1
  • FIG. 16 (b) Indicates an edge line and a luminance distribution when there is no solid object in the detection area A1.
  • the attention line La set in the tire rubber portion of the other vehicle VX is determined to be an edge line in the bird's-eye view image.
  • the luminance change of the bird's-eye view image on the attention line La is gentle. This is because the tire of the other vehicle VX is extended in the bird's-eye view image by converting the image captured by the camera 10 into a bird's-eye view image.
  • FIG. 16B it is assumed that the attention line La set in the white character portion “50” drawn on the road surface in the bird's-eye view image is erroneously determined as an edge line.
  • the edge information of the present embodiment can be positioned as “distribution information of pixels having a luminance difference equal to or greater than a predetermined threshold along the direction in which the three-dimensional object falls when the viewpoint is converted into a bird's-eye view image”.
  • the three-dimensional object detection unit 37 determines whether or not the edge line is detected by erroneous determination. When the luminance change along the edge line is larger than a predetermined threshold, the three-dimensional object detection unit 37 determines that the edge line is detected by erroneous determination. And the said edge line is not used for the detection of a solid object. Thereby, white characters such as “50” on the road surface, weeds on the road shoulder, and the like are determined as edge lines, and the detection accuracy of the three-dimensional object is prevented from being lowered.
  • the three-dimensional object detection unit 37 calculates the luminance change of the edge line by any one of the following mathematical formulas 4 and 5.
  • the luminance change of the edge line corresponds to the evaluation value in the vertical direction in the real space.
  • Equation 4 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 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).
  • the attribute b (xi, yi) of the attention point Pa (xi, yi) is “1”. Become. If the relationship is other than that, the attribute b (xi, yi) of the attention point Pai is '0'.
  • This threshold value t2 is set in advance by an experiment or the like in order to determine that the attention line La is not on the same three-dimensional object. Then, the three-dimensional object detection unit 37 sums up the attributes b for all the attention points Pa on the attention line La, obtains an evaluation value in the vertical equivalent direction, and determines whether the edge line is correct.
  • 17 and 18 are flowcharts showing details of the three-dimensional object detection method according to the present embodiment.
  • FIG. 17 and FIG. 18 for the sake of convenience, the processing for the detection area A1 will be described, but the same processing is executed for the detection area A2.
  • step S21 the camera 10 images a predetermined area specified by the angle of view a and the attachment position.
  • step S22 the viewpoint conversion unit 31 inputs the captured image data captured by the camera 10 in step S21, performs viewpoint conversion, and generates bird's-eye view image data.
  • step S23 the luminance difference calculation unit 35 sets the attention line La on the detection area A1. At this time, the luminance difference calculation unit 35 sets a line corresponding to a line extending in the vertical direction in the real space as the attention line La.
  • luminance difference calculation part 35 sets the reference line Lr on detection area
  • step S25 the luminance difference calculation unit 35 sets a plurality of attention points Pa on the attention line La.
  • the luminance difference calculation unit 35 sets the attention points Pa as many as not causing a problem at the time of edge detection by the edge line detection unit 36.
  • step S26 the luminance difference calculation unit 35 sets the reference point Pr so that the attention point Pa and the reference point Pr are substantially the same height in the real space. Thereby, the attention point Pa and the reference point Pr are arranged in a substantially horizontal direction, and it becomes easy to detect an edge line extending in the vertical direction in the real space.
  • step S27 the luminance difference calculation unit 35 calculates the luminance difference between the attention point Pa and the reference point Pr that have the same height in the real space.
  • the edge line detection unit 36 calculates the attribute s of each attention point Pa in accordance with Equation 1 above.
  • step S28 the edge line detection unit 36 calculates the continuity c of the attribute s of each attention point Pa in accordance with Equation 2 above.
  • step S29 the edge line detection unit 36 determines whether or not the value obtained by normalizing the total sum of continuity c is greater than the threshold value ⁇ according to the above formula 3.
  • the edge line detection unit 36 detects the attention line La as an edge line in step S30. Then, the process proceeds to step S31.
  • the edge line detection unit 36 does not detect the attention line La as an edge line, and the process proceeds to step S31.
  • This threshold value ⁇ can be set in advance, but can be changed in accordance with a control command to the control unit 39.
  • step S31 the computer 30 determines whether or not the processing in steps S23 to S30 has been executed for all the attention lines La that can be set on the detection area A1. If it is determined that the above processing has not been performed for all the attention lines La (S31: NO), the processing returns to step S23, a new attention line La is set, and the processing up to step S31 is repeated. On the other hand, when it is determined that the above process has been performed for all the attention lines La (S31: YES), the process proceeds to step S32 in FIG.
  • step S32 of FIG. 18 the three-dimensional object detection unit 37 calculates a luminance change along the edge line for each edge line detected in step S30 of FIG.
  • the three-dimensional object detection unit 37 calculates the luminance change of the edge line according to any one of the above formulas 4, 5, and 6.
  • step S33 the three-dimensional object detection unit 37 excludes edge lines whose luminance change is larger than a predetermined threshold from the edge lines. That is, it is determined that an edge line having a large luminance change is not a correct edge line, and the edge line is not used for detecting a three-dimensional object. As described above, this is to prevent characters on the road surface, roadside weeds, and the like included in the detection area A1 from being detected as edge lines. Therefore, the predetermined threshold value is a value set based on a luminance change generated by characters on the road surface, weeds on the road shoulder, or the like obtained in advance by experiments or the like.
  • the three-dimensional object detection unit 37 determines whether or not the amount of the edge line is equal to or larger than the second threshold value ⁇ .
  • the second threshold value beta may be set in advance to seek in advance by experiment or the like, can also be changed according to the control command the control unit 39 shown in FIG. 3 emitted, which will be described in detail later. For example, if you set the four-wheeled vehicle as a three-dimensional object to be detected, said second threshold value beta, are set based on the number of emerging automobiles edge lines in the detection area A1 in advance by experiment or the like.
  • the three-dimensional object detection unit 37 detects that a three-dimensional object exists in the detection area A1 in step S35.
  • the three-dimensional object detection unit 37 determines that there is no three-dimensional object in the detection area A1. Thereafter, the processing illustrated in FIGS. 17 and 18 ends.
  • the detected three-dimensional object may be determined to be another vehicle VX that travels in the adjacent lane adjacent to the lane in which the host vehicle V travels, and the relative speed of the detected three-dimensional object with respect to the host vehicle V is taken into consideration. It may be determined whether the vehicle is another vehicle VX traveling in the adjacent lane.
  • the second threshold value ⁇ can be set in advance, but can be changed according to a control command to the control unit 39.
  • the vertical direction in the real space with respect to the bird's-eye view image A vertical imaginary line is set as a line segment extending to. Then, for each of a plurality of positions along the vertical imaginary line, a luminance difference between two pixels in the vicinity of each position can be calculated, and the presence or absence of a three-dimensional object can be determined based on the continuity of the luminance difference.
  • the attention line La corresponding to the line segment extending in the vertical direction in the real space and the reference line Lr different from the attention line La are set for the detection areas A1 and A2 in the bird's-eye view image. Then, a luminance difference between the attention point Pa on the attention line La and the reference point Pr on the reference line Lr is continuously obtained along the attention line La and the reference line La. In this way, the luminance difference between the attention line La and the reference line Lr is obtained by continuously obtaining the luminance difference between the points.
  • 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 a three-dimensional object (distribution information of pixels having a luminance difference equal to or greater than a predetermined threshold) at the set position of the attention line La.
  • a three-dimensional object can be detected based on a continuous luminance difference.
  • the detection accuracy of a three-dimensional object can be improved.
  • the luminance difference between two points of approximately the same height near the vertical imaginary line is obtained.
  • the luminance difference is obtained from the attention point Pa on the attention line La and the reference point Pr on the reference line Lr, which are substantially the same height in the real space, and thus the luminance when there is an edge extending in the vertical direction. The difference can be detected clearly.
  • the attention point Pa is attributed based on the luminance difference between the attention point Pa on the attention line La and the reference point Pr on the reference line Lr, and attribute continuity c along the attention line La is obtained.
  • the attention line La to determine whether the edge line on the basis of the boundary between areas of high and low luminance area luminance is detected as an edge line, to perform edge detection along the natural human sensory Can do. This effect will be described in detail.
  • FIG. 19 is a diagram illustrating an example of an image for explaining the processing of the edge line detection unit 36.
  • 102 is an adjacent image.
  • 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.
  • the edge line detection unit 36 determines the part 103 as an edge line only when there is continuity in the attribute of the luminance difference in addition to the luminance difference in the part 103, the edge line detection unit 36 An erroneous determination of recognizing a part 103 that is not recognized as an edge line as a sensation as an edge line can be suppressed, and edge detection according to a human sensation can be performed.
  • the edge line detection unit 36 when the luminance change of the edge line detected by the edge line detection unit 36 is larger than a predetermined threshold value, it is determined that the edge line has been detected by erroneous determination.
  • the captured image acquired by the camera 10 is converted into a bird's-eye view image, the three-dimensional object included in the captured image tends to appear in the bird's-eye view image in a stretched state.
  • the luminance change of the bird's-eye view image in the stretched direction tends to be small.
  • 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.
  • the brightness change in the stretched direction tends to increase in the bird's-eye view image. Therefore, by determining the luminance change of the bird's-eye view image along the edge line as in this example, the edge line detected by the erroneous determination can be recognized, and the detection accuracy of the three-dimensional object can be improved.
  • the three-dimensional object detection device 1 of this example when detecting the three-dimensional object by the above-described two three-dimensional object detection unit 33 (or three-dimensional object detection unit 37), the three-dimensional object detection device 1 of this example includes a three-dimensional object determination unit 34 and a virtual image determination unit 38. And a control unit 39. Based on the detection result by the three-dimensional object detection unit 33 (or the three-dimensional object detection unit 37), the three-dimensional object determination unit 34 determines whether or not the detected three-dimensional object is the other vehicle VX existing in the detection areas A1 and A2. Judgment finally.
  • the three-dimensional object detection unit 33 (or the three-dimensional object detection unit 37) detects a three-dimensional object that reflects a determination result of a virtual image determination unit 38 described later.
  • the virtual image determination unit 38 determines whether or not the detected three-dimensional object is a virtual image in which an image of a building or the like is transferred to a water film or the like formed on the road surface from the result of texture analysis of the image corresponding to the detected three-dimensional object. Determine whether.
  • the control unit 39 is the other vehicle V in which the detected three-dimensional object exists in the detection areas A1 and A2.
  • a control command for controlling each unit (including the control unit 39) included in the computer 30 is output so that the determination is suppressed.
  • the solid object determination unit 34 of the present embodiment finally determines whether or not the solid object detected by the three-dimensional object detection units 33 and 37 is the other vehicle VX existing in the detection areas A1 and A2.
  • the three-dimensional object detected by the three-dimensional object determination unit 34 is determined to be the other vehicle VX existing in the detection areas A1 and A2
  • processing such as notification to the occupant is executed.
  • the three-dimensional object determination unit 34 can suppress determining that the detected three-dimensional object is the other vehicle VX according to the control command of the control unit 38.
  • the control unit 38 suppresses determining that the detected three-dimensional object is the other vehicle VX.
  • the control command to be sent is sent to the three-dimensional object determination unit 34.
  • the three-dimensional object determination unit 34 stops the determination process of the three-dimensional object according to this control command, or determines that the detected three-dimensional object is not the other vehicle VX, that is, there is no other vehicle VX in the detection areas A1 and A2. .
  • the control command is not acquired, it is possible to determine that the three-dimensional object detected by the three-dimensional object detection units 33 and 37 is the other vehicle VX existing in the detection areas A1 and A2.
  • the virtual image determination unit 38 of the present embodiment can determine whether the image of the three-dimensional object related to the detection is a virtual image based on the differential waveform information generated by the three-dimensional object detection unit 33.
  • the virtual image determination unit 38 of the present embodiment is configured such that the luminance difference of the image area of the image information corresponding to the three-dimensional object, particularly the image information corresponding to the outline of the three-dimensional object along the vertical direction is less than a predetermined value.
  • the three-dimensional object detected in the area including the image area is determined to be a virtual image.
  • the virtual image determination unit 38 determines that the frequency counted in the differential waveform information is a predetermined value among the determination lines (La to Lf in FIG. 5) along the direction in which the three-dimensional object falls when the viewpoint image of the bird's-eye view image is converted.
  • One or more comparison determinations including one determination line (Lc or Ld) adjacent to the luminance of the image area on the reference determination line (La) and the reference determination line is specified by identifying one reference determination line (for example, La) as described above. It is determined whether or not the luminance difference with the luminance of the image area on the line (Lb, Lc, Ld, Le) is less than a predetermined value.
  • the area including the image area It is determined that the three-dimensional object detected in step 1 is a virtual image.
  • the luminance difference is compared with a certain pixel on the reference determination line (La) or the luminance of an image area including the pixel and an image including the pixel including the comparison determination line (Lb, Lc, Ld, Le) or the pixel including the pixel.
  • the brightness of the area can be compared.
  • the luminance difference can be determined based on the number of pixels indicating a predetermined difference in the differential waveform information shown in FIG.
  • this image is not an image obtained from an actual three-dimensional object, but a virtual image in which the three-dimensional object is reflected in a puddle (water film) on the road surface.
  • the virtual image determination unit 38 has a predetermined number of comparison determination lines (Lb, Lc, Ld, Le) including an image region whose luminance difference from the luminance of the image region on the reference determination line (La) is less than a predetermined value.
  • Lb, Lc, Ld, Le comparison determination lines
  • FIG. 20 is a diagram showing a state in which a water pool (water film) is formed on the road surface in the detection area A2, and an image of a surrounding structure is reflected on the surface.
  • 21 and 22 show the difference waveform information DWt1 generated from the bird's-eye view image of the image of the other vehicle VX that actually exists in the detection area A1, and the image of the surrounding structure on the water film formed in the detection area A2.
  • the differential waveform information DWt2 generated from the bird's-eye view image of the image is shown. As shown in the diagram on the left side of FIG. 21 and FIG.
  • the differential waveform information DWt1 generated from the bird's-eye view image of the existing other vehicle VX has a pixel indicating a predetermined difference along the falling direction of the three-dimensional object. Is detected, and a peak corresponding to the appearance feature of the three-dimensional object is seen.
  • a virtual image in which the surrounding structure is reflected in the water film is shown.
  • the differential waveform information DWt2 generated from the bird's-eye view image there is no change in the number of pixels indicating a predetermined difference along the falling direction of the three-dimensional object, and there is no peak peculiar to the three-dimensional object.
  • whether the image corresponding to the detected three-dimensional object is a real image is a virtual image in which surrounding structures are reflected in the water film on the road surface, using the feature that the contrast is low. It can be judged whether it is a virtual image.
  • the virtual image determination unit 38 of the present embodiment can determine whether the image of the three-dimensional object related to detection is a virtual image based on the edge information.
  • the edge information used in the virtual image determination process may be information generated by the three-dimensional object detection unit 37, or may be edge information acquired separately from the captured image.
  • the virtual image determination unit 38 determines the luminance difference between adjacent image areas among the determination lines (La to Ld, Lr in FIG. 14) along the direction in which the three-dimensional object falls when the viewpoint of the bird's-eye view image is converted.
  • One reference determination line for example, Lr in which an edge equal to or greater than a predetermined threshold is detected is specified, and the luminance of the image area on the reference determination line (Lr) and the determination line (Lb ⁇ ) adjacent to the reference determination line (Lr) are identified. If the luminance difference with the luminance of the image region on one or more comparison determination lines (La to Ld) including Lc) is less than a predetermined value, the three-dimensional object detected in the region including the image region is a virtual image. Judge that there is.
  • the virtual image determination unit 38 when there are a predetermined number or more of comparison determination lines (Lb to Lc) including an image region whose luminance difference from the luminance of the image region on the reference determination line (Lr) is less than a predetermined value. Can determine that the three-dimensional object detected in the region including the image region is a virtual image. As described above, it is possible to accurately determine whether or not the image is a virtual image by verifying the presence or absence of contrast in a wide range and determining whether or not the image is a virtual image.
  • the virtual image determination unit 38 of the present embodiment determines whether the image information corresponding to the three-dimensional object detected based on the contrast of the image information in the detection area A1 and the detection area A2 is a virtual image or a real image.
  • the contrast of the image information is calculated based on the texture feature amount of the image information in the detection area A1 and the detection area A2.
  • a texture analysis method known at the time of filing can be appropriately applied as a method for extracting, evaluating, and determining the texture of image information.
  • control unit 39 When the virtual image determination unit 38 determines that the three-dimensional object detected by the three-dimensional object detection unit 33 in the previous process is a virtual image, the control unit 39 of the present embodiment performs the three-dimensional object detection unit 33, 37, a control command to be executed in any one or more of the three-dimensional object determination unit 34, the virtual image determination unit 38, or the control unit 39 that is itself can be generated.
  • the control command of the present embodiment is a command for controlling the operation of each unit so that it is suppressed that the detected three-dimensional object is the other vehicle VX. This is to prevent the virtual image in which the surrounding structure is reflected in the water film on the road surface from being erroneously determined as the other vehicle VX. Since the computer 30 of this embodiment is a computer, control commands for the three-dimensional object detection process, the three-dimensional object determination process, and the virtual image determination process may be incorporated in advance in the program of each process, or may be sent out at the time of execution.
  • the control command of the present embodiment may be a command for a result of stopping the process of determining the detected three-dimensional object as another vehicle, or determining that the detected three-dimensional object is not another vehicle,
  • the control unit 39 can output a control command for increasing the threshold value p regarding the difference between pixel values in the difference waveform information to the three-dimensional object detection unit 33.
  • the control unit 39 determines that the image information corresponding to the three-dimensional object is a virtual image in the previous process, a water film is formed in the detection areas A1 and A2, and the image information of the detection areas A1 and A2 is included in the image information. It can be determined that the surrounding structures are highly likely to be reflected. If a three-dimensional object is detected in the same manner as usual, a virtual image reflected in the water film may be erroneously detected as a real image of the other vehicle VX even though the other vehicle VX does not exist in the detection areas A1 and A2. is there.
  • the threshold value regarding the difference between the pixel values when generating the differential waveform information is changed to be high so that the three-dimensional object is difficult to be detected in the next processing.
  • the detection sensitivity is adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is hard to be detected, so the surrounding structure reflected in the water film It is possible to prevent an object from being erroneously detected as the other vehicle VX traveling in the adjacent lane.
  • the control unit 39 of the present embodiment sets the number of pixels indicating a predetermined difference on the difference image of the bird's eye view image. It is possible to output a control command for outputting a value that is counted and frequency-distributed low to the three-dimensional object detection unit 33.
  • the value obtained by counting the number of pixels indicating a predetermined difference on the difference image of the bird's-eye view image and performing frequency distribution is the value on the vertical axis of the difference waveform DW t generated in step S5 of FIG.
  • the control unit 39 can determine that there is a high possibility that a water film is formed in the detection areas A1 and A2, and therefore the detection area in the next process.
  • the frequency-distributed value of the differential waveform DW t is changed to be low so that the other vehicle VX is less likely to be erroneously detected in A1 and A2.
  • the detection sensitivity is adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is difficult to detect, so the virtual image formed on the water film is It is possible to prevent erroneous detection as the other vehicle VX traveling in the lane.
  • the control unit 39 increases the predetermined threshold value related to the luminance used when detecting the edge information when the virtual image determination unit 38 determines that the image information corresponding to the three-dimensional object is a virtual image.
  • the command is output to the three-dimensional object detection unit 37.
  • the predetermined threshold value relating to the luminance used when detecting edge information is the threshold value ⁇ for determining a value obtained by normalizing the sum of the continuity c of the attributes of each point of interest Pa in step S29 in FIG. 17, or the step in FIG. 34 is a second threshold value ⁇ for evaluating the amount of edge lines.
  • control unit 39 determines that the three-dimensional object is a virtual image in the previous process, a water film may be formed in the detection areas A1 and A2, and the surrounding structure may be reflected in the water film. Since it can be determined that it is high, the threshold value ⁇ used for detecting the edge line or the second threshold value ⁇ for evaluating the amount of the edge line is changed to be high so that the solid object is difficult to be detected in the next processing.
  • the detection sensitivity is adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is hard to be detected, so the surrounding structure reflected in the water film It is possible to prevent a virtual image of an object from being erroneously detected as another vehicle VX traveling in the adjacent lane.
  • the control unit 39 when the virtual image determination unit 38 determines that the image information corresponding to the three-dimensional object is a virtual image, the control unit 39 according to the present embodiment outputs a control command for outputting the detected amount of edge information low. Output to the detector 37.
  • the amount of the detected edge information is the amount of edge line in step 34 for each target point attribute value of the sum normalized continuity c of Pa, or 18 in step S29 in FIG. 17.
  • the control unit 39 can determine that there is a high possibility that the surrounding structure is reflected in a water film such as a puddle.
  • the value obtained by normalizing the sum of the continuity c of the attribute of each attention point Pa or the amount of the edge line is changed to be low.
  • the detection sensitivity is adjusted by reducing the output value so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is difficult to be detected. It is possible to prevent erroneous detection of the reflected virtual image of the surrounding structure as the other vehicle VX traveling in the adjacent lane.
  • control unit 39 of the present embodiment further increases any of the first threshold value ⁇ , the threshold value p, the second threshold value ⁇ , or the threshold value ⁇ when the brightness of the detection areas A1 and A2 is equal to or higher than a predetermined value.
  • a control command can be generated and output to the three-dimensional object detection units 33 and 37.
  • the brightness of the detection areas A1 and A2 can be acquired from the captured image of the camera 10. When the brightness of the detection areas A1 and A2 is higher than a predetermined value and bright, it can be determined that there is a high possibility that a water film that reflects light is formed on the detection areas A1 and A2.
  • the detection sensitivity is increased by increasing the threshold so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is not easily detected. It is possible to prevent erroneous detection of a virtual image of the surrounding structure reflected in the water film as the other vehicle VX traveling in the adjacent lane.
  • control part 39 of this embodiment acquires the moving speed of the own vehicle V from the vehicle speed sensor 20, and when the moving speed of the own vehicle V detected by the vehicle speed sensor 20 is less than a predetermined value, it is 1st.
  • a control command for further increasing the threshold value ⁇ , the threshold value p, the second threshold value ⁇ , or the threshold value ⁇ can be generated and output to the three-dimensional object detection unit. If the moving speed of the host vehicle V is low, the discriminating ability of the difference in the difference waveform information and the difference in the edge information tends to decrease. That is, when the moving speed of the host vehicle V is low, the presence of the three-dimensional object may not be accurately reflected in the difference waveform information or the edge information.
  • the threshold value is increased so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is not easily detected.
  • control unit 39 when the above-described three-dimensional object detection units 33 and 37 detect a three-dimensional object when the relative moving speed of the detected three-dimensional object with respect to the host vehicle V is within a predetermined range.
  • the detection sensitivity is adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is difficult to be detected.
  • the control unit 39 of the present embodiment sets a predetermined value range for evaluating the relative movement speed in the three-dimensional object detection units 33 and 37. A control command to reduce is generated and output to the three-dimensional object detection units 33 and 37.
  • the three-dimensional object detected in the previous process is a virtual image
  • the three-dimensional object is an image of a water film formed on the road surface, and the one detected as a three-dimensional object is assumed to be a stationary object. can do.
  • the predetermined sensitivity range used for determining whether or not the vehicle is the other vehicle VX can be narrowed to increase the detection sensitivity.
  • control unit 39 can generate a control command for reducing the predetermined value range by changing the lower limit value indicated by the negative value of the predetermined value range for evaluating the relative movement speed to a high value. .
  • control unit 39 can change the lower limit value indicated by a negative value to a high value in a predetermined value range defined as ⁇ 20 km to 100 km, and can define, for example, ⁇ 5 km to 100 km.
  • the relative movement speed indicated by a negative value is a speed that moves backward with respect to the traveling direction of the host vehicle V.
  • the three-dimensional object detected in the previous process is a virtual image
  • the three-dimensional object is an image of a water film formed on the road surface
  • the one detected as a three-dimensional object is assumed to be a stationary object. can do. Since a stationary object may flow behind the traveling host vehicle V, it is highly possible that an object traveling backward at a predetermined speed or more indicated by a negative value is a water film.
  • the lower limit value indicated by a negative value in the predetermined value range is changed to a higher value in order to eliminate this.
  • the lower limit value indicated by a negative value to a high value, it is possible to prevent a water film flowing backward at a predetermined speed or higher from being erroneously detected as the other vehicle VX.
  • the control unit 39 In adjusting the threshold relating to the speed, the control unit 39 generates a control command for further reducing the predetermined value range for evaluating the relative movement speed when the luminance of the detection areas A1 and A2 is equal to or higher than the predetermined value. It is possible to output to the three-dimensional object detection units 33 and 37.
  • the brightness of the detection areas A1 and A2 can be acquired from the image information of the camera 10 as described above. When the brightness of the detection areas A1 and A2 is higher than a predetermined value and bright, it can be determined that there is a high possibility that a water film that reflects light is formed on the detection areas A1 and A2.
  • the predetermined value range for evaluating the relative movement speed is further reduced, and the other vehicle traveling next to the traveling lane of the host vehicle V
  • the detection sensitivity so that VX is hard to be detected, it is possible to prevent erroneous detection of the virtual image of the surrounding structure reflected in the water film as the other vehicle VX traveling in the adjacent adjacent lane.
  • the control unit 39 acquires the moving speed of the host vehicle V from the vehicle speed sensor 20, and the moving speed of the host vehicle V detected by the vehicle speed sensor 20 is less than a predetermined value. Can generate a control command for further reducing the predetermined range for evaluating the relative movement speed and output the control command to the three-dimensional object detection units 33 and 37. If the moving speed of the host vehicle V is low, the discriminating ability of the difference in the difference waveform information and the difference in the edge information tends to decrease.
  • the moving speed of the host vehicle V is low, the presence of the three-dimensional object may not be accurately reflected in the difference waveform information or the edge information, and there is a tendency that objects other than the other vehicle VX are detected as a three-dimensional object.
  • the predetermined value range for evaluating the relative moving speed is further reduced and the vehicle travels next to the travel lane of the host vehicle V.
  • control unit 39 and the three-dimensional object determination unit 34 and the three-dimensional object detection units 33 and 37 that have acquired the control command will be described with reference to FIGS.
  • the process shown in FIGS. 23 to 27 is the current three-dimensional detection process performed using the result of the previous process after the previous three-dimensional object detection process.
  • the virtual image determination unit 38 determines whether or not the three-dimensional object detected by the three-dimensional object detection unit 33 is a virtual image. Whether or not the three-dimensional object is a virtual image can be determined based on the contrast of the detected image information of the three-dimensional object. In this case, it can be performed based on the difference waveform information generated by the three-dimensional object detection unit 33 described above, or can be performed based on the edge information generated by the three-dimensional object detection unit 37.
  • the virtual image determination unit 38 of the present embodiment may perform the virtual image determination process before or after the three-dimensional object detection process by the three-dimensional object detection units 33 and 37, or perform the virtual image determination process in parallel with the three-dimensional object detection process. Also good. Further, the virtual image determination unit 38 of the present embodiment may perform the virtual image determination process using edge information and difference information (pixel distribution information) used in the three-dimensional object detection process by the three-dimensional object detection units 33 and 37. Then, the virtual image determination process may be performed using other edge information or difference information (pixel distribution information) obtained from the captured image.
  • edge information and difference information pixel distribution information
  • step 42 the control unit 39 determines whether or not the detected three-dimensional object is a virtual image in the determination of the virtual image calculated in step 41.
  • the control unit 39 When the detected three-dimensional object is a virtual image, the control unit 39 outputs a control command to each unit so that it is suppressed that the detected three-dimensional object is determined to be the other vehicle VX. As an example, the process proceeds to step S46, and the control unit 39 outputs a control command with a content to stop the detection processing of the three-dimensional object to the three-dimensional object determination unit 34. As another example, the process proceeds to step S47, and the control unit 39 can determine that the detected three-dimensional object is not the other vehicle VX.
  • step S43 to perform a three-dimensional object detection process.
  • step 43 if a three-dimensional object is detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S45, and it is determined that the detected three-dimensional object is the other vehicle VX.
  • step S47 determines no other vehicle VX exists in the detection areas A1 and A2.
  • FIG. 24 shows another processing example.
  • the control unit 39 proceeds to step S51, from the threshold value p and the difference waveform information regarding the difference between the pixel values when generating the difference waveform information.
  • One or more of the first threshold value ⁇ used when determining a three-dimensional object, the threshold value ⁇ when generating edge information, and the second threshold value ⁇ used when determining a three-dimensional object from edge information are set higher.
  • a control command is sent to the three-dimensional object detection units 33 and 37.
  • the first threshold value ⁇ is used to determine the peak of the differential waveform DW t in step S7 of FIG.
  • the threshold value ⁇ is a threshold value for determining a value obtained by normalizing the sum of the continuity c of the attribute of each target point Pa in step S29 in FIG. 17, and the second threshold value ⁇ is the amount of the edge line in step 34 in FIG. Is a threshold value for evaluating.
  • the control unit 39 may generate a control command for decreasing the output value evaluated by the threshold instead of increasing the threshold and output the control command to the three-dimensional object detection units 33 and 37.
  • Other processes are the same as those shown in FIG.
  • step S52 when the control unit 39 determines that the three-dimensional object detected in step 42 is a virtual image, the control unit 39 proceeds to step S52, and the brightness of the detection areas A1 and A2 is equal to or higher than a predetermined value. It is determined whether or not.
  • the process proceeds to step S53, and a control command for further increasing the threshold value in step S51 of FIG. 24 may be generated and output to the three-dimensional object detection units 33 and 37.
  • the control unit 39 may generate a control command for further reducing the output value evaluated by the threshold instead of increasing the threshold and output the control command to the three-dimensional object detection units 33 and 37.
  • Other processes are the same as those shown in FIG.
  • step S54 when it is determined that the three-dimensional object detected in step 42 is a virtual image, the control unit 39 proceeds to step S54 and the moving speed of the host vehicle is less than a predetermined value. Determine whether or not.
  • the process proceeds to step S55, and a control command for further increasing the threshold value in step S51 of FIG. 24 may be generated and output to the three-dimensional object detection units 33 and 37.
  • the control unit 39 generates a control command for further lowering the output value evaluated by the threshold instead of increasing the threshold, and the other processes are the same as those shown in FIG.
  • the control unit 39 When the output value is lowered, the control unit 39 counts the number of pixels indicating a predetermined difference on the difference image of the bird's eye view image and outputs a control command for outputting the frequency distribution value lower. Output to the detector 33.
  • the value obtained by counting the number of pixels indicating a predetermined difference on the difference image of the bird's-eye view image and performing frequency distribution is the value on the vertical axis of the difference waveform DW t generated in step S5 of FIG.
  • the control unit 39 can output a control command for outputting a low amount of detected edge information to the three-dimensional object detection unit 37.
  • the detected amount of edge information is a value obtained by normalizing the sum of the continuity c of the attributes of each point of interest Pa in step S29 in FIG.
  • the control unit 39 can determine that a water film is formed in the detection areas A1 and A2, and in the next process, the three-dimensional object is determined as a three-dimensional object.
  • a control command for changing a value obtained by normalizing the sum of the continuity c of the attribute of each attention point Pa or changing the amount of the edge line so as to make it difficult to detect an object can be output to the three-dimensional object detection unit 37.
  • FIG. 27 shows still another processing example. If it is determined that the three-dimensional object detected in step 42 is a virtual image, the control unit 39 proceeds to step S61, generates a control command for reducing the predetermined range for evaluating the relative movement speed, and generates a three-dimensional object. It outputs to the object detection parts 33 and 37. Incidentally, when the relative moving speed of the detected three-dimensional object with respect to the own vehicle is within a predetermined range, the three-dimensional object detection units 33 and 37 detect the detection result as a three-dimensional object as a detection target such as another vehicle. 34.
  • the three-dimensional object detection device 1 of the present embodiment when the image of the three-dimensional object detected in the predetermined detection areas A1 and A2 behind the host vehicle V is a virtual image, By presuming that there is a high possibility that light is reflected in the resulting puddle and the surrounding buildings are reflected, and by suppressing the three-dimensional objects in the detection areas A1, A2 from being other vehicles The determination as to whether the three-dimensional object is the other vehicle VX can be made strictly. As a result, it is possible to provide a three-dimensional object detection device that detects the other vehicle VX traveling in the detection areas A1 and A2 with high accuracy.
  • differential waveform information is generated from a bird's-eye view image, and one reference determination line whose frequency counted in the differential waveform information is equal to or greater than a predetermined value is specified. If the luminance difference between the luminance of the image area on the reference determination line and the luminance of the image area on one or more comparison determination lines including the determination line adjacent to the reference determination line is less than a predetermined value, the area including the image area Since it is determined that the three-dimensional object detected in step 3 is a virtual image, the other vehicle VX traveling in the adjacent lane can be accurately detected.
  • the luminances of image areas adjacent to each other among the determination lines along the direction in which the three-dimensional object falls when the bird's-eye view image is subjected to viewpoint conversion An image on one or more comparison determination lines that includes one reference determination line in which edge information having a difference equal to or greater than a predetermined threshold is detected, and includes a determination line adjacent to the luminance of the image area on the reference determination line and the reference determination line If the brightness difference from the brightness of the area is less than a predetermined value, it is determined that the three-dimensional object detected in the area including the image area is a virtual image, so the other vehicle VX traveling in the adjacent lane is accurately detected. be able to.
  • the three-dimensional object detection device 1 of the present embodiment when it is determined in the previous process that the image information corresponding to the three-dimensional object is a virtual image, by changing the first threshold value ⁇ to a higher value, Since the detection sensitivity is adjusted so that the other vehicle VX traveling next to the traveling lane of the host vehicle V is not easily detected, the surrounding structure reflected in the water film is erroneously detected as the other vehicle VX traveling in the adjacent lane. Can be prevented.
  • the second threshold value ⁇ for evaluating the amount of edge lines. Because the detection sensitivity is adjusted so that the other vehicle VX traveling next to the driving lane of the own vehicle V is difficult to detect, the virtual image of the surrounding structure reflected in the water film is displayed in the adjacent lane. It is possible to prevent erroneous detection as the traveling other vehicle VX.
  • the three-dimensional object detection device 1 of the present embodiment when the brightness of the detection areas A1 and A2 is higher than a predetermined value, the other vehicle VX traveling next to the traveling lane of the host vehicle V is detected.
  • the detection sensitivity By adjusting the detection sensitivity by increasing the threshold so as to be difficult, it is possible to prevent false detection of the virtual image of the surrounding structure reflected in the water film as another vehicle VX traveling in the adjacent lane. .
  • the detection process of the three-dimensional object is stopped, so that the detection areas A1, A2
  • the surrounding structure reflected in the water film is prevented from being erroneously detected as another vehicle VX traveling in the adjacent lane adjacent to the traveling lane of the host vehicle V. can do.
  • the three-dimensional object detection device 1 of the present embodiment when the moving speed of the host vehicle is less than the predetermined value, the other vehicle VX traveling next to the traveling lane of the host vehicle V is unlikely to be detected.
  • the detection sensitivity by adjusting the detection sensitivity by increasing the threshold, it is possible to prevent erroneous detection of a virtual image of the surrounding structure reflected in the water film as the other vehicle VX traveling in the adjacent lane.
  • the three-dimensional object detection device 1 of the present embodiment when the virtual image determination unit 38 determines that the three-dimensional object is a virtual image, the three-dimensional object is an image of a water film formed on the road surface, and the three-dimensional object Since it can be assumed that what is detected as an object is a stationary object, a control command for reducing a predetermined range for evaluating the relative movement speed in the three-dimensional object detection units 33 and 37 is generated, and the three-dimensional object detection 33 is generated. , 37, the predetermined range of the relative movement speed used for determining whether or not the vehicle is another vehicle VX is narrowed, the detection sensitivity is increased, and a stationary object is not erroneously detected as the other vehicle VX. can do.
  • the predetermined value range for evaluating the relative movement speed is further reduced to reduce the own vehicle.
  • the predetermined value range for evaluating the relative moving speed is further reduced to reduce the vehicle V
  • the virtual image of the surrounding structure reflected in the water film travels in the adjacent lane by adjusting the detection sensitivity by increasing the threshold so that other vehicles VX traveling next to the travel lane are difficult to detect. It is possible to prevent erroneous detection as the other vehicle VX.
  • the three-dimensional object detection device 1 of the present embodiment when there are a predetermined number or more of comparison determination lines including an image area whose luminance difference from the luminance of the image area on the reference determination line is less than a predetermined value, Since it is determined that the three-dimensional object detected in the area including the image area is a virtual image, whether or not it is a virtual image is verified by verifying whether or not there is a contrast in a wide range and determining whether or not it is a virtual image. Judgment can be made accurately.
  • the camera 10 corresponds to an imaging unit according to the present invention
  • the viewpoint conversion unit 31 corresponds to an image conversion unit according to the present invention
  • the alignment unit 32 and the three-dimensional object detection unit 33 include a three-dimensional object detection according to the present invention.
  • the brightness difference calculation unit 35, the edge line detection unit 36, and the three-dimensional object detection unit 37 correspond to a three-dimensional object detection unit according to the present invention
  • the three-dimensional object determination unit 34 corresponds to a three-dimensional object determination unit.
  • the virtual image determination unit 38 corresponds to a virtual image determination unit
  • the control unit 39 corresponds to a control unit
  • the vehicle speed sensor 20 corresponds to a vehicle speed sensor.
  • SYMBOLS 1 Three-dimensional object detection apparatus 10 ... Camera 20 ... Vehicle speed sensor 30 ... Computer 31 ... Viewpoint conversion part 32 ... Position alignment part 33, 37 ... Three-dimensional object detection part 34 ... Three-dimensional object judgment part 35 ... Luminance difference calculation part 36 ... Edge detection Section 38 ... Virtual image determination section 39 ... Control section 40 ... Smear detection section a ... Angle of view A1, A2 ... Detection area CP ... Intersection DP ... Difference pixels DW t , DW t '... Difference waveforms DW t1 to DW m , DW m + k to DW tn ... small areas L1 and L2 ... ground lines La and Lb ...

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)
  • Traffic Control Systems (AREA)
  • Image Analysis (AREA)

Abstract

L'invention porte sur ce dispositif de détection d'objet solide qui comprend les éléments suivants : une caméra (10) qui prend l'image de la zone située derrière un véhicule ; une unité de détection d'objet solide (33) qui détecte un objet solide sur la base d'informations de forme d'onde de différence générées à partir de nombres de pixels indiquant une différence prescrite dans la direction dans laquelle ledit objet solide tourne lors d'une transformation en une image à vol d'oiseau ; une unité de détermination d'objet solide (34) qui détermine si l'objet solide détecté est un autre véhicule ou non ; une unité de détermination d'image virtuelle (38) qui détermine que l'objet solide est une image virtuelle si la différence entre la luminosité dans une région d'image sur une ligne d'évaluation de base, qui est la partie d'une ligne d'évaluation s'étendant dans la direction de rotation mentionnée ci-dessus de l'objet solide, une fréquence comptée dans les informations de forme d'onde de différence étant supérieure ou égale à une valeur prescrite, et la luminosité dans une région d'image sur une ligne d'évaluation de comparaison, qui comprend une partie de la ligne d'évaluation adjacente à la ligne d'évaluation de base, est inférieure à une valeur prescrite ; et une unité de commande (39) qui empêche l'objet solide détecté d'être identifié comme étant un autre véhicule s'il a été déterminé que ledit objet solide est une image virtuelle.
PCT/JP2013/052476 2012-02-16 2013-02-04 Dispositif de détection d'objet solide et procédé de détection d'objet solide WO2013121911A1 (fr)

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JP2015216462A (ja) * 2014-05-08 2015-12-03 日産自動車株式会社 立体物検出装置
CN108345858A (zh) * 2018-02-11 2018-07-31 杭州鸿泉物联网技术股份有限公司 一种车辆载重状态检测方法和系统
CN109073778A (zh) * 2016-04-22 2018-12-21 株式会社电装 物体检测装置、物体检测方法
JP7460393B2 (ja) 2020-02-27 2024-04-02 フォルシアクラリオン・エレクトロニクス株式会社 車両用外界認識装置

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JP2005234999A (ja) * 2004-02-20 2005-09-02 Fuji Heavy Ind Ltd 車両用運転支援装置
JP2010191888A (ja) * 2009-02-20 2010-09-02 Toshiba Corp 画像処理装置及び交通監視装置
JP2012003662A (ja) * 2010-06-21 2012-01-05 Nissan Motor Co Ltd 移動距離検出装置及び移動距離検出方法

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Publication number Priority date Publication date Assignee Title
JP2002321579A (ja) * 2001-04-26 2002-11-05 Sumitomo Electric Ind Ltd 警告情報生成方法及び車両側方映像生成装置
JP2005234999A (ja) * 2004-02-20 2005-09-02 Fuji Heavy Ind Ltd 車両用運転支援装置
JP2010191888A (ja) * 2009-02-20 2010-09-02 Toshiba Corp 画像処理装置及び交通監視装置
JP2012003662A (ja) * 2010-06-21 2012-01-05 Nissan Motor Co Ltd 移動距離検出装置及び移動距離検出方法

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015216462A (ja) * 2014-05-08 2015-12-03 日産自動車株式会社 立体物検出装置
CN109073778A (zh) * 2016-04-22 2018-12-21 株式会社电装 物体检测装置、物体检测方法
CN109073778B (zh) * 2016-04-22 2020-02-28 株式会社电装 物体检测装置、物体检测方法
CN108345858A (zh) * 2018-02-11 2018-07-31 杭州鸿泉物联网技术股份有限公司 一种车辆载重状态检测方法和系统
JP7460393B2 (ja) 2020-02-27 2024-04-02 フォルシアクラリオン・エレクトロニクス株式会社 車両用外界認識装置

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