WO2013125335A1 - Dispositif de détection d'objet solide et procédé pour la détection d'un objet solide - Google Patents

Dispositif de détection d'objet solide et procédé pour la détection d'un objet solide Download PDF

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Publication number
WO2013125335A1
WO2013125335A1 PCT/JP2013/052477 JP2013052477W WO2013125335A1 WO 2013125335 A1 WO2013125335 A1 WO 2013125335A1 JP 2013052477 W JP2013052477 W JP 2013052477W WO 2013125335 A1 WO2013125335 A1 WO 2013125335A1
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Prior art keywords
detection
dimensional object
vehicle
detection area
region
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PCT/JP2013/052477
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English (en)
Japanese (ja)
Inventor
早川　泰久
修 深田
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日産自動車株式会社
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Priority to JP2014500637A priority Critical patent/JP5794379B2/ja
Publication of WO2013125335A1 publication Critical patent/WO2013125335A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/215—Motion-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20224—Image subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle
    • G06T2207/30261—Obstacle

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. 2012-036300 filed on Feb. 22, 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.
  • An obstacle detection apparatus that detects an obstacle by performing overhead conversion of an image obtained by capturing an image of the surroundings of a vehicle and using a difference between two overhead conversion images that are temporally different (see Patent Document 1).
  • an object such as an implantation or guardrail provided on the shoulder of the traveling lane of the own vehicle Is erroneously recognized as an image of another vehicle traveling in the adjacent lane.
  • an image of an object placed on the road shoulder enters an area corresponding to the adjacent lane, and thus the above problem is likely to occur.
  • the problem to be solved by the present invention is to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle, and traveling in the adjacent lane.
  • Another object of the present invention is to provide a three-dimensional object detection device that can detect other vehicles with high accuracy.
  • the present invention sets a first detection area when calculating the movement distance of a three-dimensional object, and a second detection area having a smaller area than the first detection area when detecting a three-dimensional object existing in the detection area.
  • the present invention sets a relatively wide first detection area when calculating the moving distance of a three-dimensional object, and sets a relatively narrow second detection area when detecting the presence of a three-dimensional object, To prevent the object on the shoulder of the driving lane of the host vehicle from being included in the detection area, and to reduce the possibility that the feature of the object appearing in a predetermined cycle along the shoulder is misidentified as the feature of the other vehicle in the adjacent lane Accordingly, it is possible to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle. As a result, it is possible to provide a three-dimensional object detection device that detects, with high accuracy, other vehicles that travel in the adjacent lane adjacent to the travel lane of the host vehicle.
  • 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; 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. 26 is a first diagram for explaining the processing of FIG. 25.
  • FIG. 26 is a second diagram for explaining the processing of FIG. 25.
  • It is a flowchart of the further another example which shows the control procedure of the setting of a detection area. It is a figure for demonstrating the process of FIG.
  • 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 the present example includes a camera 10, a vehicle speed sensor 20, and a calculator 30.
  • 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, a positioning unit 32, a three-dimensional object detection unit 33, a detection area setting unit 34, and a smear detection unit 40.
  • the calculation unit 30 of the present embodiment has a configuration relating to a three-dimensional object detection block using differential waveform information.
  • the calculation unit 30 of the present embodiment can also be configured with respect to a three-dimensional object detection block using edge information.
  • a block configuration A configured by the alignment unit 32 and the three-dimensional object detection unit 33 is surrounded by a broken line, a luminance difference calculation unit 35, an edge line detection unit 36, It can be configured by replacing the block configuration B configured by the three-dimensional object detection unit 37.
  • both the block configuration A and the block configuration B can be provided, so that the solid object can be detected using the difference waveform information and the solid object can be detected using the edge information.
  • the block configuration A and the block configuration B can be provided, either the block configuration A or the block configuration B can be operated according to environmental factors such as brightness. Each configuration will be described below.
  • the three-dimensional object detection device 1 of the present embodiment exists in the detection area A1 of the right adjacent lane or the detection area A2 of the left adjacent lane behind the vehicle based on image information obtained by the monocular camera 1 that captures the rear of the vehicle. A three-dimensional object is detected.
  • the detection area setting unit 34 sets detection areas A1 and A2 in the captured image information and on the right and left sides behind the host vehicle V, respectively.
  • the positions of the detection areas A2 and A2 are not particularly limited, and can be set as appropriate according to the processing conditions.
  • the detection region setting unit 34 of the present embodiment includes a first detection region having a relatively large area and a second detection region having a relatively small area for each of the detection regions A1 and A2, and the first detection region Either the region or the second detection region can be selected and set.
  • the detection area setting unit 34 of the present embodiment selects and sets a second detection area having a relatively small area, and detects the speed of the three-dimensional object. In this case, the first detection region having a relatively large area is selected and set.
  • the three-dimensional object detection unit 33 determines the presence / absence of the three-dimensional object based on the image information of the second detection region having a relatively small area set by the detection region setting unit 34.
  • the moving speed is calculated based on the image information of the first detection area having a relatively large area set by the detection area setting unit 34.
  • the detection area setting unit 34 can set the first detection area and the second detection area in response to a request from the three-dimensional object detection unit 33 (37).
  • the detection area setting unit 34 of the present embodiment sets the second detection area of the detection area A1 set on the right side behind the vehicle on the right side behind the vehicle.
  • the first detection area the area including the apex located at the right end on the front side of the vehicle is deleted, and the second detection area of the detection area A2 set on the left side on the rear side of the vehicle is set on the left side on the rear side of the vehicle. It is assumed that the region including the apex located at the left end on the front side of the vehicle in the set first detection region is a missing region.
  • the detection region setting unit 34 of the present embodiment selects and sets a second detection region in which a region including a vertex located at the right end or the left end is missing.
  • the first detection region in which the region including the vertex is not lost is selected and set.
  • the detection area setting unit 34 of the present embodiment sets the first detection area and the position of the camera 10 in the lateral direction of the vehicle from the first detection area.
  • a detection area including a second detection area in which the distance is greater than or equal to a predetermined distance, that is, an area outside the first detection area (the road shoulder side) is missing (narrowed in the vehicle width direction) is set.
  • the viewpoint conversion unit 31 inputs captured image data of a predetermined area obtained by imaging with the camera 10 and converts the input captured image data into a bird's-eye image data in a bird's-eye view state.
  • the state viewed from a bird's-eye view is a state viewed from the viewpoint of a virtual camera looking down from above, for example, vertically downward.
  • This viewpoint conversion can be executed as described in, for example, Japanese Patent Application Laid-Open No. 2008-219063.
  • the viewpoint conversion of captured image data to bird's-eye view image data is based on the principle that a vertical edge peculiar to a three-dimensional object is converted into a straight line group passing through a specific fixed point by viewpoint conversion to bird's-eye view image data. This is because a planar object and a three-dimensional object can be distinguished if used. Note that the result of the image conversion processing by the viewpoint conversion unit 31 is also used in detection of a three-dimensional object by edge information described later.
  • the alignment unit 32 sequentially inputs the bird's-eye image data obtained by the viewpoint conversion of the viewpoint conversion unit 31, and aligns the positions of the inputted bird's-eye image data at different times.
  • 4A and 4B are diagrams for explaining the outline of the processing of the alignment unit 32, where FIG. 4A is a plan view showing the moving state of the host vehicle V, and FIG. 4B is an image showing the outline of the alignment.
  • 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 .
  • 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. 5 is determined and plotted as the count number at the intersection CP.
  • 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.
  • 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.
  • step S0 the computer 30 sets a detection area based on a predetermined rule. This detection area setting method will be described in detail later.
  • the computer 30 receives data of the image P captured by the camera 10 and generates a smear image SP by the smear detection unit 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 of the previous time, and the data of the smear bird's-eye view image SB t and the smear bird's-eye view of the previous time.
  • 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 smear S generation region 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 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.
  • an example of performing a process of calculating a moving speed (movement distance) of a three-dimensional object after a process of determining whether or not a three-dimensional object exists in the detection region is shown, but this order is not particularly limited. That is, when the moving speed of the three-dimensional object is within the predetermined value range, the three-dimensional object detection process may be performed based on the difference waveform information. In addition, the three-dimensional object detection (presence / absence) process and the three-dimensional object movement speed calculation process may be performed in parallel.
  • 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.
  • 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 vehicle speed of the host vehicle V is determined based on a signal from the vehicle speed sensor 20, but the present invention is not limited to this, and the speed may be estimated from a plurality of images at different times. In this case, a vehicle speed sensor becomes unnecessary, and the configuration can be simplified.
  • 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), 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. Note that the detection area setting unit 34 in the present embodiment can also set the detection areas A1 and A2 by the method described above.
  • the distance d1 is a distance from the host vehicle V to the ground lines L1 and L2.
  • the ground lines L1 and L2 mean lines on which a three-dimensional object existing in the lane adjacent to the lane in which the host vehicle V travels contacts the ground.
  • the purpose of the present embodiment is to detect other vehicles VX and the like (including two-wheeled vehicles) traveling in the left and right lanes adjacent to the lane of the host vehicle V on the rear side of the host vehicle V.
  • 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.
  • the luminance difference calculation unit 35 calculates a luminance difference with respect to the bird's-eye view image data subjected to viewpoint conversion by the viewpoint conversion unit 31 in order to detect the edge of the three-dimensional object included in the bird's-eye view image. For each of a plurality of positions along a vertical imaginary line extending in the vertical direction in the real space, the brightness difference calculating unit 35 calculates a brightness difference between two pixels in the vicinity of each position.
  • the luminance difference calculation unit 35 can calculate the luminance difference by either a method of setting only one vertical virtual line extending in the vertical direction in the real space or a method of setting two vertical virtual lines.
  • 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. For example, the luminance difference calculation unit 35 sets the reference line Lr as the reference line Lr in the previous processing, sets the reference line Lr for the attention line La, and sequentially obtains the luminance difference. It will be.
  • 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 attribute s (xi, yi) of the attention point Pai is “ ⁇ 1”.
  • 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 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 illustrating the luminance distribution of the edge line.
  • FIG. 16A illustrates the edge line and the luminance distribution when another vehicle VX as a three-dimensional object exists in the detection area A1, and
  • FIG. Indicates an edge line and a luminance distribution when there is no solid object in the detection area A1.
  • 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.
  • 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 brightness change of the bird's-eye view image on the attention line La has a large undulation. This is because a portion with high brightness in white characters and a portion with low brightness such as a road surface are mixed on the edge line.
  • 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 S20 the computer 30 sets a detection area based on a predetermined rule. This detection area setting method will be described in detail later.
  • step S21 the camera 10 captures an image of 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.
  • 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.
  • step S34 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 ⁇ is set based on the number of edge lines of the four-wheeled vehicle that have appeared in the detection region A1 in advance through experiments or the like.
  • 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 (S38).
  • 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. Specifically, when a three-dimensional object is detected in step S35, the process proceeds to step S36, and it is determined whether or not the moving speed of the three-dimensional object is within a predetermined value range.
  • step S35 it is determined whether or not the three-dimensional object detected in step S35 is another vehicle traveling in the adjacent lane.
  • the moving speed of the three-dimensional object can be obtained based on the moving speed of the edge on the image.
  • step S36 if the moving speed of the three-dimensional object detected in step S35 is within a predetermined value, the process proceeds to step S37, and the three-dimensional object is determined as another vehicle.
  • the process proceeds to step S39, and it is determined that the three-dimensional object is not another vehicle.
  • the predetermined range of the moving speed when determining whether or not the three-dimensional object is another vehicle is that the absolute moving speed is 10 km / h or more and the own vehicle of the three-dimensional object as described in the processing of FIG.
  • the relative movement speed with respect to V can be +60 km / h or less.
  • the predetermined value range of the moving speed it may be defined that the absolute moving speed is a positive value (not a negative value) or not 0 km / h. Thereafter, the processing illustrated in FIGS. 17 and 18 ends.
  • an example of performing a process of calculating a moving speed (movement distance) of a three-dimensional object after a process of determining whether or not a three-dimensional object exists in the detection region is shown, but this order is not particularly limited. That is, when the moving speed of the three-dimensional object is within the predetermined value range, the three-dimensional object detection process may be performed based on the edge information. In addition, the three-dimensional object detection (presence / absence) process and the three-dimensional object movement speed calculation process may be performed simultaneously in parallel.
  • 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. In the case where the luminance difference between the attention line La and the reference line Lr is high, there is a high possibility that there is an edge of the three-dimensional object at the set position of the attention line La.
  • 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.
  • 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 units 33 and 37 send detection results to an external vehicle controller for further notification to the occupant and vehicle control.
  • the three-dimensional object detection device 1 of the present example is a three-dimensional object detected by the above-described two three-dimensional object detection units 33 (or the three-dimensional object detection unit 37), and the three-dimensional object is a detection target.
  • a determination unit that finally determines whether the vehicle is a vehicle VX can also be provided.
  • the detection region setting unit 34 includes the first detection region and the second detection region having different areas for the detection regions A1 and A2. Specifically, the detection region setting unit 34 sets the first detection regions A11 and A21 when the three-dimensional object detection units 33 and 37 calculate the movement distance of the three-dimensional object existing in the detection regions A1 and A2, and the three-dimensional object detection unit 33 and 37 sets the first detection regions A11 and A21. When the object detection units 33 and 37 detect solid objects existing in the detection areas A1 and A2, the second detection areas A12 and A22 having a smaller area than the first detection areas A11 and A21 are set.
  • the detection area setting unit 34 has a predetermined first detection area and a distance in the lateral direction of the vehicle from the installation position of the camera 10 in the first detection area for each of the detection areas A1 and A2. And a second detection region in which a band-like region outside the first detection region (on the shoulder side) is lost.
  • the second detection area is an area in which the roadside side of the first detection area is moved to the vehicle side and the width of the detection area is narrowed in the vehicle width direction.
  • the first detection area of the present embodiment may be defined as an area common to the default detection area. The mode of this detection area will be described later with reference to FIG.
  • the three-dimensional object detection device 1 detects the presence of a three-dimensional object and the moving speed based on image information of a captured image behind the vehicle acquired by the in-vehicle camera 10.
  • an image of an object such as a planting or guardrail provided on the shoulder of the traveling lane of the host vehicle is erroneously displayed in the adjacent lane.
  • an image of an object such as a planting or guardrail provided on the shoulder of the traveling lane of the host vehicle is erroneously displayed in the adjacent lane.
  • it is mistaken as an image of another vehicle traveling on the road. As shown in FIG.
  • the guard rail Q2 or curb stone Q2 having the same shape on the shoulder
  • the image information includes feature points like the same object even though it is a different object when periodically photographed by the camera 10. May end up.
  • the shape of the planting / grass Q1 is not constant, the feature points included in the image information may accidentally resemble the feature points of the other vehicle VX.
  • the guardrail / curbstone Q2 since the guard rail / curbstone Q2 having the same shape is repeatedly arranged, the guardrail / curbstone differs depending on the combination of the imaging period of the camera 10, the vehicle speed of the host vehicle V, and the unit length of the repeated pattern of the guardrail / curbstone Q2.
  • the image information of Q2 may indicate a feature point like the same object, or the feature point included in the image information of the guardrail / curbstone Q2 may accidentally resemble the feature point of the other vehicle VX.
  • the objects other than the detection target such as the planting / grass Q1 or the guardrail / curbstone Q2 provided on the shoulder of the traveling lane of the own vehicle V
  • the video is mistakenly mistaken as the video of the other vehicle VX traveling in the adjacent lane.
  • the traveling lane of the host vehicle is narrow, the image of the object on the road shoulder enters the region corresponding to the adjacent lane, and thus the above problem is likely to occur.
  • the viewpoint of the image is converted into a bird's-eye view image, the image spreads horizontally and the image of the object on the road shoulder enters the detection areas A1 and A2, and the above problem is likely to occur.
  • the three-dimensional object detection device 1 prepares two detection areas A11, A12, A21, and A22 having different areas for each of the detection areas A1 and A2, and the detection areas according to the processing characteristics. Switch.
  • the detection area setting unit 34 sets the relatively wide first detection areas A11 and A21 when the three-dimensional object detection units 33 and 37 calculate the movement distance of the three-dimensional object, and the three-dimensional object detection unit 33. , 37 detect the three-dimensional object existing in the detection areas A1 and A2, the second detection areas A12 and A22 having a smaller area than the first detection areas A11 and A21 are set.
  • the host vehicle V is moving and solid objects (including other vehicles VX) may also move, the calculation of the speed (movement distance per hour) of the three-dimensional object is not expected to be performed over a wide range.
  • the detection result cannot be obtained.
  • the detection areas A1 and A2 when detecting the moving speed of the three-dimensional object need to set a distance of a predetermined value or more along the traveling direction of the host vehicle V.
  • the detection areas A1 and A2 it is necessary to set the detection areas A1 and A2 to be narrow in order to eliminate the influence when the image information includes images of roadside shoulder planting, grass, guardrails, curbs, and the like.
  • the detection areas A1 and A2 when detecting the moving speed of the three-dimensional object are set to a distance less than a predetermined value along the traveling direction of the host vehicle V, or the detection areas A1 and A2 are in the vehicle width direction of the host vehicle V. It is preferable that the distance is less than a predetermined value along.
  • first detection areas A11, A21 and second detection areas A12, A22 By setting these two first detection areas A11, A21 and second detection areas A12, A22 according to the processing scene, while accurately grasping the moving speed of the three-dimensional object, planting the road shoulder, grass,
  • the other vehicle VX can be detected with high accuracy without erroneously detecting a guardrail, a curbstone, or the like as the other vehicle VX.
  • the first detection region A11 and the second detection region A12, or the first detection region A21 and the second detection region A22 may have the same area and the same form, or may have different areas and different forms. Good.
  • the detection area A1 of the present embodiment has a first detection area A11 and a second detection area A12.
  • the detection area A1 will be described as an example, but the same applies to the first detection area A21 and the second detection area A22 of the detection area A2.
  • the rear distance from the camera 10 is a distance along the + y direction in the vehicle length direction, and the lateral distance from the camera 10 is along the + x in the vehicle width direction. Distance.
  • the detection areas A11, A12, A21, and A22 are one aspect of the detection area set by the detection area setting unit 34, and are not particularly limited as long as they are set on the right side and the left side behind the vehicle.
  • the detection area setting unit 34 of the three-dimensional object detection device may set the first detection areas A11 and A21 and the second detection areas A21 and A22 that are the same as the predetermined detection area set by default, for example, set by default.
  • the first detection areas A11 and A21 and the second detection areas A21 and A22 included in the predetermined detection area may be set.
  • the area A13 in which the rear distance (the distance in the + y direction) from the position of the camera 10 in the first detection area 11 is a predetermined distance or more is lost ( (Removed) area.
  • the length D2 of the second detection area A12 along the traveling direction of the host vehicle V is shorter than the length D1 of the first detection area A11.
  • the relationship between the lengths D2 and D1 can be appropriately set according to the size of the detection areas A1 and A2.
  • the second detection region A12 as a region in which the rear distance from the camera 10 is a predetermined distance or more in the first detection region A11, planting of the road shoulder, grass, guardrail, It is difficult to extract periodic features from images such as curbs, the probability that feature points like the same object will be included in the image information, and the feature points included in the image information are the feature points of the other vehicle VX. The probability of being similar can be reduced.
  • the second detection area A12 of this example is an area A13 in which the lateral distance (distance in the ⁇ x direction) from the position of the camera 10 in the first detection area A11 is a predetermined distance or more. This is a region where is missing. That is, the second detection areas A12 and A21 are areas where the areas outside the first detection areas A11 and A21 (the side away from the vehicle) are excluded. As shown in the figure, the length W2 of the second detection area A12 along the vehicle width direction of the host vehicle V is shorter than the length W1 of the first detection area A11. The relationship between the lengths W2 and W1 can be appropriately set according to the sizes of the detection areas A1 and A2. In this aspect, as shown in FIG.
  • the predetermined lateral distance from the position of the camera 10 can gradually increase from W3 to W2, and further to W1, as the distance from the camera 10 increases.
  • the second detection region A12 as a region in which the side distance from the camera 10 in the first detection region 11 is greater than or equal to a predetermined distance, planting roadsides, grass, guardrails, Since it is possible to mask images such as curbs so that they are not included in the image information used for detection processing, whether or not they are solid objects such as roadside planting, grass, guardrails, curbs, etc. It can be made not to judge.
  • the rear distance from the camera 10 in the first detection area 11 is within a predetermined distance, and the lateral distance from the camera 10 is greater than or equal to the predetermined distance.
  • the length D2 of the second detection region A12 along the traveling direction of the host vehicle V is shorter than the length D1 of the first detection region A11 and the second detection region A12 extends along the vehicle width direction of the host vehicle V.
  • the length W2 of the second detection area A12 is shorter than the length W1 of the first detection area A11. D1.
  • the relationship between D2, W1, and W2 is not particularly limited, and can be set as appropriate according to the sizes of the detection areas A1 and A2. Further, in this aspect, the area of the region A13 to be deleted is not particularly limited as long as the vertex E1 on the outer front side of the first detection region A11 is included, and the distance between W2 and D2 can be arbitrarily set.
  • the second detection area A22 set in the detection area A2 on the right side behind the vehicle shown in FIG. 24 is located at the right end on the vehicle front side in the first detection area A21 set in the same detection area A2.
  • the second detection area A12 set in the detection area A1 on the left side of the rear of the vehicle is an area in which the area A23 including the vertex E2 that is positioned is lost, and is the first detection area A11 set in the same detection area A1
  • the region A13 to be deleted in the first detection region A11 may include at least the vertex E1
  • the A23 to be deleted in the first detection region A21 may include at least the vertex E2.
  • the other vehicle VX can be detected with high accuracy without erroneously detecting road shoulder planting, grass, guardrail, curbstone, etc. as the other vehicle VX. Can do.
  • the detection area setting section 34 can greatly expand the areas of the second detection areas A12 and A22.
  • the detection area setting unit 34 deletes the area A13 including the vertex E1 and the area A23 including the vertex E2, the areas of the second detection areas A12 and A22 are determined as the first detection area.
  • the area can be the same as A11 and A21.
  • FIG. 25 shows a control procedure when this processing is performed.
  • the process illustrated in FIG. 25 is the current three-dimensional detection process performed using the result of the previous process after the previous three-dimensional object detection process. That is, as described above, the detection area setting unit 34 of the present embodiment detects the presence of a three-dimensional object using the image information of the first detection areas A11 and A21 when detecting the moving speed of the three-dimensional object. At this time, each detection area is set so as to use the image information of the second detection areas A12 and A22.
  • FIG. 26 shows the second detection areas A12 and A22 in the normal state.
  • step S46 the second detection areas A12 and A22 having a relatively small area are expanded.
  • FIG. 27 shows the expanded second detection areas A12 ′ and A22 ′.
  • the rear distance from the camera 10 of the second detection areas A12 and A22 is extended from D21 to D21 ′.
  • the second detection areas A12 and A22 are extended along the traveling direction of the vehicle (the rear distance of the camera 10), but the second detection area is small in the vehicle width direction as in the example shown in FIG.
  • the second detection areas A12 and A22 may be expanded by extending the distance W in the vehicle width direction.
  • the detection area setting unit 34 sets the first detection areas A11 and A21 and the extended second detection areas A12 and A22.
  • the three-dimensional object other vehicle VX
  • priority is given to preventing false detection of planting and guardrails as other vehicles, but in the enhanced monitoring state where other vehicles are approaching, false detection Priority is given to continuously detecting other vehicles over prevention.
  • the expanded second detection areas A12 and A22 are set, so that the three-dimensional object can be detected carefully and the accuracy of detection of the other vehicle VX is determined. Can be increased.
  • step S43 to perform a solid object detection process.
  • This three-dimensional object detection processing is performed according to the above-described processing using the difference waveform information of FIGS. 11 and 12 by the three-dimensional object detection unit 33 or the processing using the edge information of FIGS. 17 and 18 by the three-dimensional object detection unit 37. Is called.
  • 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 On the other hand, when a solid object is not detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S47, and it is determined that no other vehicle VX exists in the detection areas A1 and A2.
  • FIG. 28 is a flowchart showing another control procedure.
  • the process illustrated in FIG. 28 is a process of setting a detection area using the calculation result of the moving speed of the three-dimensional object.
  • the movement speed of the three-dimensional object may be performed using the result of the previous three-dimensional object detection process, or may be performed using the result of the movement speed calculation process performed in parallel with the three-dimensional object detection process.
  • the detection area setting unit 34 of the present embodiment uses the image information of the first detection areas A11 and A21 when detecting the moving speed of the three-dimensional object, and the second detection area when detecting the presence of the three-dimensional object.
  • the image information of A12 and A22 is used.
  • FIG. 22 described above shows the second detection areas A12 and A22 and the first detection areas A11 and A21 in the normal state.
  • step S51 If it is determined in step S51 that the moving speed of the three-dimensional object calculated based on the moving distance is within the predetermined threshold and it is highly likely that the three-dimensional object is another vehicle, the process proceeds to step S46.
  • step S46 the second detection areas A12 and A22 whose widths are narrowed are expanded. Specifically, when the moving speed of the three-dimensional object is other than a predetermined value, it can be determined that the three-dimensional object is not another vehicle. For this reason, in this embodiment, in order to give priority to improving the accuracy of detection of a three-dimensional object, the second detection areas A12 and A22 are arranged as cameras in the first detection areas A11 and A21 as shown in FIG.
  • the second detection areas A12 and A22 have a predetermined distance W2 + q (> W2) in the lateral direction W of the vehicle from the installation position of the camera 10 in the first detection areas A11 and A21.
  • the above region is a missing region. That is, the area to be deleted is reduced and the second detection areas A12 and A22 are expanded.
  • the second detection region in this case may have the same area and shape as the first detection regions A11 and A21.
  • the detection area setting unit 34 sets the first detection areas A11, A21 and the extended second detection areas A12, A22.
  • the moving speed of the three-dimensional object is outside the predetermined value range, it is considered that the three-dimensional object is unlikely to be another vehicle, so priority is given to preventing erroneous detection of planting or guardrail as another vehicle.
  • the moving speed of the three-dimensional object is within the predetermined range, it is considered that the three-dimensional object is likely to be another vehicle. Therefore, it is preferable to perform the detection process with the maximum detection range.
  • the accurate detection result of the already detected other vehicle (three-dimensional object) is continuously acquired rather than prevention of erroneous detection.
  • the second detection areas A12 and A22 in which the road-side belt-like area is initially lost are expanded. Therefore, the other vehicle (three-dimensional object) once detected can be monitored with high accuracy while preventing the three-dimensional object from being erroneously detected due to the influence of the roadside structure.
  • step S43 to perform a solid object detection process.
  • This three-dimensional object detection processing is performed according to the above-described processing using the difference waveform information of FIGS. 11 and 12 by the three-dimensional object detection unit 33 or the processing using the edge information of FIGS. 17 and 18 by the three-dimensional object detection unit 37. Is called.
  • 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 On the other hand, when a solid object is not detected in the detection areas A1 and A2 by the three-dimensional object detection units 33 and 37, the process proceeds to step S47, and it is determined that no other vehicle VX exists in the detection areas A1 and A2.
  • the three-dimensional object detection device 1 of the present embodiment when calculating the movement distance of the three-dimensional object, the relatively wide first detection areas A11 and A21 are set, and the presence of the three-dimensional object Since the second detection areas A12 and A22 that are relatively narrow are set when the vehicle is detected, it is possible to prevent an object on the road shoulder of the traveling lane of the host vehicle from being included in the detection area and at a predetermined cycle along the road shoulder. The possibility that the feature of the appearing object is mistaken as the feature of the other vehicle VX in the adjacent lane can be reduced.
  • the other vehicle VX can be detected with high accuracy without erroneously detecting a guardrail, a curbstone, or the like as the other vehicle VX.
  • the second detection region A12 is a region in which the region A13 whose back distance from the camera 10 is greater than or equal to a predetermined distance in the first detection region 11 is lost. This makes it difficult to extract periodic features from images of roadside planting, grass, guardrails, curbs, etc., and the probability that feature points like the same object will be included in the image information, and image information It is possible to reduce the probability that the feature points included are similar to the feature points of the other vehicle VX. As a result, it is possible to prevent the object on the shoulder of the traveling lane of the own vehicle from being erroneously detected as another vehicle traveling in the adjacent lane adjacent to the traveling lane of the own vehicle. Other vehicles traveling in the lane can be detected with high accuracy.
  • the second detection area A12 is defined as an area in which the area A13 whose lateral distance from the camera 10 is greater than or equal to a predetermined distance in the first detection area 11 is lost.
  • images of roadside planting, grass, guardrail, curb, etc. can be excluded from the image information used for detection processing, so in the first place, roadside planting, grass, guardrail, curb, etc. It is possible not to determine whether or not it is a three-dimensional object.
  • the second detection areas A12 and A22 are missing the area E1 including the vertex located at the right end on the vehicle front side of the first detection areas A11 and A21.
  • the second detection areas A12, A22 are the first detection areas A11. Since the region including the vertex E2 located at the left end of the vehicle front side in A21 is missing, the same effect as the above (2) and (3) is obtained, and the moving speed of the three-dimensional object is accurately grasped. However, it is possible to detect the other vehicle VX with high accuracy without erroneously detecting planting of a road shoulder, grass, guardrail, curbstone, and the like as the other vehicle VX.
  • the first detection area set by the detection area setting unit 34 when calculating the movement distance of the three-dimensional object is similarly set by the detection area setting unit 34 on each of the right and left sides behind the vehicle. For example, by making the detection area the same size as the default detection area, the calculation load can be reduced and a stable detection result can be derived.
  • the expanded second detection areas A12 and A22 are set, so that the three-dimensional object is carefully detected. And the accuracy of detection of the other vehicle VX can be increased.
  • the three-dimensional object detection process is performed based on the image information of the second detection region in which the roadside region is missing, and the movement distance (movement speed of the three-dimensional object) ) Is calculated based on the image information of the first detection area having no defect, and based on the moving speed of the three-dimensional object, it is determined that the three-dimensional object is likely to be another vehicle.
  • the missing part of the second detection area is reduced, that is, the second detection area is expanded to the roadside.
  • 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 luminance 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 detection region setting unit 34 corresponds to a detection region setting 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 ... Detection area setting part 35 ... Luminance difference calculation part 36 ... Edge detection Section 50 ... Steering angle sensor 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 regions L1, L2 ... ground line La, Lb ...

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Abstract

La présente invention se rapporte à un dispositif de détection d'objet solide et à un procédé pour la détection d'un objet solide. Le dispositif de détection d'objet solide selon l'invention comprend : un module de paramétrage de zone de détection (34), qui est utilisé afin de paramétrer une zone de détection vers l'arrière d'un véhicule, pour le côté droit et le côté gauche du véhicule; et un module de détection d'objet solide (33, 37), qui est utilisé afin de détecter la présence d'un objet solide à l'intérieur d'une zone de détection sur le côté droit (A1) ou à l'intérieur d'une zone de détection sur le côté gauche (A2) vers l'arrière d'un véhicule, sur la base de données d'image relatives au côté arrière du véhicule qui sont transmises par une caméra (10). Le module de paramétrage de zone de détection (34) paramètre une première zone de détection (A11, A21) quand le module de détection d'objet solide (33, 37) calcule la distance sur laquelle un objet solide se déplace; et il paramètre une seconde zone de détection (A12, A22), qui est de plus petite dimension que la première zone de détection (A11, A21), quand le module de détection d'objet solide (33, 37) détecte la présence d'un corps solide à l'intérieur de la zone de détection (A1, A2).
PCT/JP2013/052477 2012-02-22 2013-02-04 Dispositif de détection d'objet solide et procédé pour la détection d'un objet solide WO2013125335A1 (fr)

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Publication number Priority date Publication date Assignee Title
WO2021166576A1 (fr) * 2020-02-17 2021-08-26 フォルシアクラリオン・エレクトロニクス株式会社 Dispositif de détection d'objet tridimensionnel, système embarqué sur véhicule et procédé de détection d'objet tridimensionnel
JP2021128671A (ja) * 2020-02-17 2021-09-02 フォルシアクラリオン・エレクトロニクス株式会社 立体物検出装置、車載システム、及び立体物検出方法
JP2021128672A (ja) * 2020-02-17 2021-09-02 フォルシアクラリオン・エレクトロニクス株式会社 立体物検出装置、車載システム、及び立体物検出方法

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JPH07220194A (ja) * 1994-02-07 1995-08-18 Fujitsu Ltd 道路環境認識装置
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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JPH07220194A (ja) * 1994-02-07 1995-08-18 Fujitsu Ltd 道路環境認識装置
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
WO2021166576A1 (fr) * 2020-02-17 2021-08-26 フォルシアクラリオン・エレクトロニクス株式会社 Dispositif de détection d'objet tridimensionnel, système embarqué sur véhicule et procédé de détection d'objet tridimensionnel
JP2021128671A (ja) * 2020-02-17 2021-09-02 フォルシアクラリオン・エレクトロニクス株式会社 立体物検出装置、車載システム、及び立体物検出方法
JP2021128672A (ja) * 2020-02-17 2021-09-02 フォルシアクラリオン・エレクトロニクス株式会社 立体物検出装置、車載システム、及び立体物検出方法
JP7356371B2 (ja) 2020-02-17 2023-10-04 フォルシアクラリオン・エレクトロニクス株式会社 立体物検出装置、車載システム、及び立体物検出方法
JP7356372B2 (ja) 2020-02-17 2023-10-04 フォルシアクラリオン・エレクトロニクス株式会社 立体物検出装置、車載システム、及び立体物検出方法

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