JP3632313B2 - Lane detection method and apparatus on road surface - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a method and apparatus for detecting a lane on a road surface, and in particular, detects a lane on a road surface by specifying boundary lines on both sides based on image information in a visible region that coincides with a driver's field of view in front of a car. The present invention relates to a method and apparatus for detecting lanes.
[0002]
[Prior art]
In general, when the road boundary line is specified geometrically, the positions of the white lines on both sides (left and right) of the road are associated with each other. Here, “associating” means that when obtaining the tangent line of the left and right white lines, the position where the normal line toward the center of curvature is shared in the road plane is determined. The intersection of the normal and the tangent is called “corresponding point”, and the line segment connecting the corresponding points on the left and right is called “road segment”. Thus, when road segments are obtained at a plurality of positions on the road and arranged in parallel, a road plane is represented, and each road segment indicates the direction of the road at the segment position.
[0003]
Regarding the method for identifying the above road boundary line, a paper written by Keio University and Shinji Ozawa “Determining Corresponding Points in Road Image Analysis” (The Institute of Electronics, Information and Communication Engineers Journal D-II Vol. J72-D-II No. 5pp. 827-830 issued in May 1989), the tangent vector T of the lane is obtained using the position of the infinity point, and the unit segment vector S that is the normal to the left and right tangent vectors is obtained as shown in FIG. Thus, a method of searching for a combination of tangent vectors where S · T = 0 has been proposed.
[0004]
The University of Maryland, D. DeMenthon's paper “A ZERO-BANK ALGORITHM FOR INVERSE PERSPECTIVE OF ROAD FROM A SINGLE IMAGE”, Proc. IEEE Inters. The following method is proposed in March 1987). That is, if the left and right tangent lines of the road boundary line are parallel, the normal line from one tangent line is also the normal line in the other tangent line, and satisfies the road segment condition. For example, as shown in FIG. 14, when the left and right tangents N1-N2 and M1-M2 are not parallel, the intersection R deviates from the expected position as the end point of the road segment with respect to the tangent N1-N2. Therefore, it is described that when the road is curved, the center of curvature and the lane width can be calculated using the normal from the left and right tangents.
[0005]
On the other hand, an image processing apparatus that processes image information in the visible region in front of an automobile and uses it for various purposes has been proposed. Japanese Laid-Open Patent Publication No. 6-215137 detects feature point information in an image at high speed. It is an object of the present invention to provide an image processing apparatus that can perform scanning, and a scanning area is determined by a combination of scanning line information (scanning start coordinates, scanning end coordinates) written on the scanning table memory means. Specifically, when detecting the outline of a white line (lane) heading in the direction of travel, the area where the white line exists is usually limited to a narrow range, so only the image data in that narrow range is detected. In order to scan, a scanning area (referred to as a window) is determined by a combination of scanning line information (scanning start coordinates, scanning end coordinates) written on the scanning table memory means.
[0006]
[Problems to be solved by the invention]
Regarding the method for specifying the road boundary line described above, in the method described in the former paper, it is necessary to repeat the operation until S · T = 0, so that there remains a problem in responsiveness. Further, in the boundary line specifying method described in the latter paper, there is no mention of how to obtain a tangent for detecting a correct road segment.
[0007]
In order to obtain such a road segment, it is ultimately necessary to cut out the edges of the left and right white lines within the same distance range and obtain the tangent of the road boundary line in that section. For example, on a straight road, a set of white lines on the left and right within the same distance range, on a curved road, a set of white lines within the same distance, that is, a section of concentric arcs separated by the same angle with respect to the same center of curvature. It is necessary to cut out a set of white lines. However, when the left and right white line edges are not in the same distance distance section, the combination of the white line edge sections in which the left and right tangent lines are parallel is searched, but the calculation is inevitably repeated.
[0008]
In the above-mentioned paper, road boundaries, road segments, etc. are expressed. However, in reality, two light-colored lines such as white that are parallel to each other with a certain distance on the road surface (representative) Then, white lines) are applied to form boundary lines on both sides (left and right), and a region between these boundary lines serves as a lane (hereinafter referred to as a lane) for driving an automobile or the like. Accordingly, the lane on the road surface can be detected by specifying the two white lines. From this point, in the present application, “lane segment” or simply “segment” is used instead of “road segment”, and “lane boundary” is used instead of “road boundary”. Accordingly, white lines are provided at both ends of the road, and when a road is detected by specifying these, it means a lane, that is, a road.
[0009]
Therefore, an object of the present invention is to provide a lane detection method that can set a segment without performing repetitive calculations and can quickly and appropriately detect a lane on a road surface.
[0010]
Another object of the present invention is to provide a lane detection device that can quickly and appropriately detect a lane on a road surface.
[0011]
[Means for Solving the Problems]
In order to solve the above problems, the road lane detection method of the present invention is based on the density of image information including the boundary lines on both sides of the lane on the road surface to be detected, as described in claim 1. Edges representing the boundary lines on both sides are detected and sequentially stored in an edge memory to form two consecutive rows of edge groups representing the boundary lines on both sides; a three-dimensional plane based on at least the two rows of edge groups Identifying the boundary lines on both sides above; identifying the center of curvature for the boundary lines on both sides based on the previous image that identified the boundary lines on both sides; and storing the boundary lines on both sides newly in the edge memory; A pair of edge sequences corresponding to a predetermined distance are extracted from the edge groups forming two consecutive edge groups to be represented along a concentric arc centered on the center of curvature. When each Identifying a pair of tangents to the column; identifying a centroid of an edge group constituting the extracted pair of edge sequences; identifying a normal to the pair of tangents passing through the centroid; and determining the normal and the pair of tangents An intersection point is specified, and a line segment between the pair of intersection points is set as a segment of the lane; an image specifying the boundary lines on both sides based on the segment is updated.
[0012]
The road surface lane detection apparatus according to the present invention, as described in claim 2 and shown in FIG. 2, inputs image information including boundary lines on both sides of the lane on the road surface to be detected. Edge detection means ED for detecting edges based on the shading of the two sides, sequentially outputting edges representing the boundary lines on both sides, and sequentially storing the edges representing the boundary lines on both sides output by the edge detection means ED; Edge memory means EM for forming two consecutive rows of edges representing the boundary lines, image processing means DP for identifying the boundary lines on both sides on a three-dimensional plane based on at least the two rows of edge groups, Curvature center specifying means RC for specifying the center of curvature with respect to the boundary lines on both sides based on the previous image specifying the boundary lines on both sides, and two consecutive rows representing the boundary lines on the both sides newly stored by the edge memory means EM of Edge sequence extracting means ES for extracting a pair of edge sequences for a predetermined distance along the concentric arc centered on the center of curvature from the edge groups forming the wedge group, and edge sequence extracting means ES The tangent specifying means TL for specifying a pair of tangents to each edge row when the pair of edge rows are parallel, and the centroid of the edge group constituting the pair of edge rows extracted by the edge row extracting means ES are specified. The center of gravity specifying means GC, the normal specifying means NL for specifying the normal to the pair of tangents passing through the center of gravity, the intersection of the normal and the pair of tangents are specified, and the line segment between the pair of intersections is determined. A segment setting unit SD that sets the segment as a lane segment and outputs the segment to the image processing unit DP, and the image processing unit DP identifies the boundary lines on both sides based on the segment. Were those configured to update the image. In this lane detection apparatus, it is assumed that the imaging means CS that images the visible region including the detection target is provided, and the image information captured by the imaging means CS is input to the edge detection means ED so as to detect the edge. Configure.
[0013]
DETAILED DESCRIPTION OF THE INVENTION
Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 explains the principle of a lane detection method according to an embodiment of the present invention. First, (1) it is assumed that the lane shape of the road surface to be detected can be specified based on the previous screen information. An edge row is extracted along a concentric arc from the edge group for the white line on the new screen with respect to the center of curvature C. Next, (2) a tangent equation for each edge row on both sides (left and right) is obtained. At this time, the constraint that the left and right white lines are parallel is provided, and two tangent equations are obtained by the least square method, and the center of gravity G of the edge group is obtained. Based on these, (3) an equation of a straight line that passes through the center of gravity G of the edge group and is orthogonal to the left and right tangents, that is, a normal line is obtained, and the intersection of the normal and the left and right tangents is obtained. (4) A line segment connecting the left and right intersections is set as a lane segment Si, and (5) the image specifying the lane shape is updated based on the segment Si. Thus, the segment can be obtained without repeated calculation.
[0014]
Furthermore, the lane shape can be specified geometrically based on the segments obtained in a plurality of sections. That is, although not shown in the figure, by sequentially obtaining a plurality of segments S1 to Sn from the front toward the far side, the linear determination of the lane, the average curve curvature radius of the arbitrary section, the azimuth ahead of the arbitrary distance, the automobile The yaw angle, lateral displacement, lane width, etc. can be calculated. If the road distances from the driver's viewpoint position to the segments S1 to Sn are D1 to Dn, the intersection point on the extension line of the segments Si and Sj is the center of curvature C in the section of the road distances Di and Dj, and the center of curvature. The distance between C and the segments Si and Sj can be expressed by the curvature radii Ri and Rj. In this way, the radius of curvature in an arbitrary section of the segment can be calculated. If a curve is applied to the right end point, left end point, or set of midpoints of the obtained segment, the boundary line or center line can be expressed as a curve equation. The boundary line or center line direction can be obtained. Further, if the above-described curve is extended to the viewpoint position and a segment at this position is obtained, the yaw angle, lateral displacement, and lane width at the viewpoint position can be calculated.
[0015]
Next, an embodiment of the lane detection device of the present invention will be described with reference to the drawings. The present embodiment is configured to reliably detect and display an image of a detection target existing in a forward visible region that matches the field of view of the driver of the automobile. FIG. 3 shows the overall configuration of the lane detection apparatus. For example, a CCD camera 1 using a CCD of a solid-state image sensor is arranged in the vicinity of a driver's viewpoint in, for example, an automobile (not shown), and a visible region in front is shown. Imaged. An analog image signal picked up by the CCD camera 1 is supplied to the AD converter 11 of the edge detector 10 and converted into a digital signal corresponding to the density.
[0016]
In the edge detection unit 10, the output signal of the AD converter 11 is supplied to the Sobel edge extraction operator 14 in the detection circuit 13 via the two line buffers 12 so that the edges in the x and y directions are detected. It is configured. The output signal of the CCD camera 1 is supplied to the xy address converter 15 as a synchronization signal. At this time, 10 bits are assigned as the x coordinate of the edge, 9 bits are assigned to the y coordinate, 8 bits are assigned for the maximum value, and 1 bit is assigned for the positive and negative signs. Further, the detection circuit 13 includes a window gate 16 described later, and is connected to the detection target determination unit 20. The edge detection unit 10 can be composed of a gate array.
[0017]
The detection target determination unit 20 is composed of a microcomputer, and includes a CPU 21, ROM 22, RAM 23, output serial port 24, interrupt counter 25, timer 26, etc., which are connected via an address bus and a data / control bus. Has been. Reference numeral 27 denotes a power supply unit. As shown in FIG. 4, the RAM 23 has a window memory 23w and an edge memory 23e. The former window data is supplied to the window gate 16, and the extraction data of the edge extraction operator 14 is supplied to the window gate 16. These logical products (AND) are calculated in 16 and the calculation result is supplied to and stored in the edge memory 23e.
[0018]
FIG. 9 shows an example of the table memory in the window memory 23w, and 0 corresponding to 480 scanning lines in the horizontal or vertical direction (the horizontal x-axis direction in the example of FIG. 9) shown on the right side of FIG. Addresses from address 479 are set. For each address, the x-coordinate or y-coordinate of at least one reference point (in the example of FIG. 9, X0 to X479 of the x-coordinate) is set for each address, and a predetermined width ( In the example of FIG. 9, W0 to W479) are set and stored. The coordinates of the upper left corner of the screen in FIG. 9 are (0, 0), and the coordinates of the lower right corner are (639, 479). When the table memory is 16 bits, 10 bits are used for setting the x coordinate of the window and 6 bits are used for setting the width. , The width can also be set almost full screen.
[0019]
FIG. 4 is a functional block diagram relating to edge detection. The synchronization signal S is sent through the horizontal counter 17 and the vertical counter 18 to the two line buffers 12, 3 × 3 matrix register 19, Sobel edge extraction operators 14ex, 14ey, respectively. The peak detectors 14px and 14py are supplied. Thus, the image data of the three scanning lines is supplied to the 3 × 3 matrix register 19 via the two line buffers 12, and the edge extraction operators 14ex and 14ey perform the x-direction and y-direction on the two-dimensional screen. Edges are detected, and the peak values obtained by the peak detectors 14px and 14py are supplied to the edge memory 23e. Note that a peak value detection memory 23p is provided to obtain the peak value in the y direction. On the other hand, a logical product (AND) of the contents of the peak value detection memory 23p when the peak is determined by the window gate 16 and the memory value of the window memory 23w is obtained, and the position and width of the window are supplied to the edge memory 23e. Is done. The edge memory 23e has density memories 23gx and 23gy in the x direction and y direction and coordinate memories 23px and 23py in the x direction and y direction, and stores peak values of the edges, respectively.
[0020]
In the lane detection apparatus of the present embodiment configured as described above, image processing is performed according to the flowcharts of FIGS. First, in step 101 of FIG. 5, all output image signals of the CCD camera 1 are supplied to the detection circuit 13 via the AD converter 11 and the line buffer 12, and image information is captured. Next, at step 102, an edge is detected. In this embodiment, a feature point is set based on this edge as shown in FIG. 10, and the x coordinate and y coordinate of this feature point are specified. In step 103, a detection target window for the current image processing is set. This process will be described later with reference to FIG.
[0021]
FIG. 10 shows the above-described feature point extraction situation. First, a signal representing the image density is differentiated, and the differential value (peak value) is binarized by a predetermined threshold value, and feature regions Z1, Z2 having a predetermined width. Is formed. Further, a thinning process is performed, and the positions of the central portions of the feature regions Z1 and Z2 are set as feature points P1 and P2, which are used as a reference representing the contour. As described above, in this embodiment, feature points are set in addition to general edges as indices representing contours, but the edges may be used as they are.
[0022]
Subsequently, in step 104, the logical product (AND) of the feature point set based on the edge in step 102 and the detection target window set in step 103 is taken, and the feature point in this window is extracted. The Then, the process proceeds to step 105. Step 105 and subsequent steps are processing that is unique when the detection target is a curve or a straight line, and is specifically suitable for specifying the white line of the lane for automobile travel targeted by this embodiment. It is a serious process.
[0023]
In step 105, as shown in FIGS. 11 and 12, the position of the feature point is converted to a position on the three-dimensional plane so that the feature point on the screen extracted in step 104 exists on the road surface. . Note that the coordinate axes of the three-dimensional plane are represented by X, Y, and Z with respect to the coordinate axes x and y of the two-dimensional plane described above. The segment of the lane is specified in step 106 based on the feature point converted to the position on the three-dimensional plane as described above, which will be described later with reference to FIGS. Subsequently, in step 107, a mathematical model is applied to the segment arrangement, and the process further proceeds to step 108, where parameters (curvature radius, lane width, etc.) representing the lane shape are measured by the matched mathematical model. Further, the process proceeds to step 109, where the parameter representing the lane shape is converted into a two-dimensional plane coordinate and used for setting the next detection target window.
[0024]
Next, setting of the detection target window executed in step 103 will be described with reference to FIG. In step 201, values for setting the upper end and the lower end of the vertical axis (y axis) of the detection target window (hereinafter simply referred to as a window) are set to predetermined values yb and yt, respectively (in this embodiment, yb ≧ 0, yt). ≦ 479), the value of the position y on the vertical axis (y-axis) is set to the lowest end of the screen (y = 479).
[0025]
Subsequently, in step 203, the left-side setting memory (y) and right-side setting memory (y) of the window are canceled (invalid), and in step 204, the value of the position y on the y-axis is decremented (−1). ) Is compared with the predetermined value yb in step 205, and steps 203 and 204 are repeated until the predetermined value yb or less is reached. When the value of the position y on the y-axis becomes equal to or smaller than the predetermined value yb, the process proceeds to step 206, where the predetermined value yb is set. In this way, the lower end of the screen is set, and processing from step 207 onward is started from a position yb below the screen.
[0026]
In step 207, the window width is set to w (y). The window width w (y) may be constant (w (y) = Ky, where Ky is a constant), or may be set so that the window width is constant on a three-dimensional plane. Or you may set so that the width | variety of a window may expand in a distant place. In step 208, the starting point of the memory (y) for setting the left side of the window is set to fl (y) -w (y) / 2. Here, fl (y) represents the center line of the white line shape on the left side on the two-dimensional screen, and is the previous value.
[0027]
In step 209, the width of the memory (y) for setting the left side of the window is set to w (y). Similarly, in step 210, the starting point of the memory (y) for setting the right side of the window is set to fr (y) −w (y) / 2, and in step 211, the memory for setting the right side of the window (y) Is set to w (y). Fr (y) represents the center line of the right white line shape on the two-dimensional screen, and is the previous value. As described above, in this embodiment, the window shape is defined by the reference point (start point) and the width, but the start point and the end point may be set.
[0028]
Next, after the value of the position y on the y-axis is decremented (−1) in step 212, it is compared with a predetermined value yt representing the upper end of the screen in step 213, and steps 207 to 207 until it falls below the predetermined value yt. 212 is repeated. When the value of the position y on the y axis falls below the predetermined value yt, the process proceeds to step 214.
[0029]
Thus, since the upper end preset in the screen is exceeded, the left-side setting memory (y) and right-side setting memory (y) of the window are canceled in step 214, and in step 215 on the y-axis. After the value of position y has been decremented (−1), it is compared with 0 in step 216, and steps 214 and 215 are repeated until the value of position y on the y-axis is less than 0. When the value of y falls below 0, the routine returns to the routine of FIG.
[0030]
The setting of the lane segments in step 106 in FIG. 5 is executed according to the flowcharts in FIGS. 7 and 8. First, in step 301, the linear shape of the white line displayed in the previous image is determined. If it is determined that the white line is curved, the process proceeds to step 302 and thereafter, and if it is determined to be a straight line, the process proceeds to step 316 and later (FIG. 8). . If it is determined that the white line is curved, after the number i representing the segment is cleared in step 302, the start point of the area of the segment number i is set to IP1 in step 303 with respect to the white line inside the curve. At 304, the end point of the area of segment number i is set to IP2.
[0031]
Next, in step 305, a straight line passing through the center C and the start point IP1 when the white line displayed in the previous image is an arc as shown in FIG. 1 is set to L1, and in step 306, the line passes through the center C and the end point IP2. The straight line is L2. Subsequently, the process proceeds to steps 307 and 308, where the intersection of the straight line L1 and the white line outside the curve is OP1, and the intersection of the straight line L2 and the white line outside the curve is OP2.
[0032]
In step 309, the coordinates of the edge group forming the white line between the intersection point IP1 and the intersection point IP2 are provided to the calculation matrix, and in step 310, the edge forming the white line between the intersection point OP1 and the intersection point OP2 Each coordinate of the group is provided to a calculation matrix. Based on these calculation results, in step 311, parallel lines are fitted to the edge group constituting the pair of edge rows by the method of least squares, and the pair of tangent lines are specified. In step 312, the centroid G of the edge group constituting the pair of edge rows is obtained, and the normal of the parallel line model passing through the centroid G is set to Si in step 313. That is, the line segment between the parallel line models is the lane segment Si. After the above processing is performed, the segment number i is incremented (+1), and the processing from step 303 to step 314 is repeated until it reaches n.
[0033]
On the other hand, if it is determined in step 301 that the linear shape of the white line displayed in the previous image is a straight line, the number i representing the segment is cleared in step 316 in FIG. In step 318, the start point of the segment number i area is set to RP1, and in step 318, the end point of the segment number i area is set to RP2. Subsequently, the process proceeds to steps 319 and 320, where the start point of the segment number i area is LP1 and the end point of the segment number i area is LP2 with respect to the white line on the left side.
[0034]
In step 321, the coordinates of the edge group constituting the white line between the start point RP1 and the end point RP2 are provided to the least squares matrix, and in step 322, the white line between the start point LP1 and the end point LP2 is displayed. Each coordinate of the edge group which comprises is used for the least squares method matrix. Based on these calculation results, in step 323, parallel lines are fitted to the edge group constituting the pair of edge rows by the least square method, and the pair of tangent lines are specified. In step 324, the centroid G of the edge group constituting the pair of edge rows is obtained, and the normal of the parallel line model passing through the centroid G is set to Si in step 325. Thus, a line segment between the parallel line models of the normal line Si is defined as a lane segment. After the above processing is performed, the segment number i is incremented (+1), and the processing from step 317 to step 326 is repeated until it reaches n.
[0035]
As described above, in this embodiment, the lane segments are set without performing repetitive calculations, so that the configuration can be made inexpensively. In addition, since the constraint is that the tangent of the white line on both sides is a parallel line, it is less susceptible to noise such as dirt on the road surface and white line, compared to the previous method of obtaining the tangent for each white line. Segments can be set. Furthermore, if the constraint condition that “the lane width is constant” is added, it becomes less susceptible to noise.
[0036]
【The invention's effect】
Since the present invention is configured as described above, the following effects can be obtained. In other words, the road lane detection method according to claim 1 specifies a pair of tangents to each edge row when the pair of extracted edge rows are parallel, and the edge groups constituting the pair of edge rows are identified. Since the center of gravity is specified, the normal to a pair of tangents passing through this center of gravity is specified, the intersection of this normal and a pair of tangents is specified, and the line segment between the pair of intersections is set as a lane segment The lane detection can be performed quickly and appropriately without being affected by noise.
[0037]
The road surface lane detection device according to claim 2 includes a tangent specifying unit that specifies a pair of tangents to each edge row when the pair of edge rows extracted by the edge row extraction unit are parallel, and an edge A segment setting means, comprising: a centroid specifying means for specifying the centroid of the edge group constituting the pair of edge rows extracted by the row extracting means; and a normal specifying means for specifying a normal to the pair of tangents passing through the centroid. To identify the intersection of the normal line and the pair of tangents, and set the line segment between the pair of intersections as a lane segment. Can be detected.
[Brief description of the drawings]
FIG. 1 is an explanatory diagram showing a segment setting method in a lane detection method on a road surface according to the present invention.
FIG. 2 is a block diagram showing an outline of a configuration of a lane detection device on a road surface according to the present invention.
FIG. 3 is a block diagram showing an overall configuration of an embodiment of the present invention.
FIG. 4 is a block diagram illustrating a configuration of an edge detection unit according to an embodiment of the present invention.
FIG. 5 is a flowchart showing image processing in an embodiment of the present invention.
FIG. 6 is a flowchart showing detection target window setting processing in an embodiment of the present invention.
FIG. 7 is a flowchart showing segment setting processing in an embodiment of the present invention.
FIG. 8 is a flowchart showing segment setting processing in an embodiment of the present invention.
FIG. 9 is a configuration diagram showing a window setting memory according to an embodiment of the present invention.
FIG. 10 is a graph illustrating a feature point detection state according to an embodiment of the present invention.
FIG. 11 is a diagram illustrating a situation in which the position of a feature point is converted to a position on a three-dimensional plane in an embodiment of the present invention.
FIG. 12 is a diagram illustrating a situation in which the position of a feature point is converted to a position on a three-dimensional plane in an embodiment of the present invention.
FIG. 13 is a diagram illustrating an example of a conventional road segment setting method.
FIG. 14 is a diagram for explaining another example of a conventional road segment setting method.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1 CCD camera 10 Edge detection part 11 AD converter 12 Line buffer 13 Detection circuit 14 Edge extraction operator 15 xy address conversion part 16 Window gate 20 Detection target determination part 23w Window memory 23e Edge memory

Claims (2)

Based on the density of the image information including the boundary lines on both sides of the lane on the road surface to be detected, the edges representing the boundary lines on both sides are detected and sequentially stored in the edge memory, and the boundary lines on both sides are continuously represented. Forming two rows of edge groups; identifying boundary lines on both sides on a three-dimensional plane based on at least the two rows of edge groups; and defining boundary lines on both sides based on a previous image identifying the boundary lines on both sides A center of curvature for a line is identified; among the edge groups that are newly stored in the edge memory and form two successive rows of edges representing the boundary lines on both sides, along a concentric arc centered on the center of curvature A pair of edge rows corresponding to a predetermined distance are extracted; a pair of tangents to each edge row is specified when the extracted pair of edge rows are parallel; a pair of edge groups constituting the extracted pair of edge rows Identify the center of gravity Specifying a normal to the pair of tangents passing through the center of gravity; specifying an intersection of the normal and the pair of tangents; and setting a line segment between the pair of intersections as a segment of the lane; A method for detecting a lane on a road surface, comprising: updating an image specifying boundary lines on both sides based on the image. Edge detection means for inputting image information including boundary lines on both sides of the lane on the road surface to be detected, detecting edges based on the density of the image information, and sequentially outputting edges representing the boundary lines on both sides; and Edge memory means for sequentially storing edges representing the boundary lines on both sides output by the edge detection means, and forming two consecutive rows of edge groups representing the boundary lines on both sides; and at least the two rows of edge groups Image processing means for specifying the boundary lines on both sides on a three-dimensional plane, curvature center specifying means for specifying the center of curvature for the boundary lines on both sides based on the previous image specifying the boundary lines on both sides, Out of edge groups newly formed by the edge memory means and forming two consecutive rows of edge groups representing the boundary lines on both sides, a predetermined distance is obtained along a concentric arc centered on the center of curvature. Edge sequence extracting means for extracting the edge sequence, tangent specifying means for specifying a pair of tangents to each edge sequence when the pair of edge sequences extracted by the edge sequence extraction means are parallel, and the edge sequence extraction Centroid specifying means for specifying the centroid of the edge group constituting the pair of edge sequences extracted by the means, normal specifying means for specifying a normal to the pair of tangents passing through the centroid, the normal and the pair of pairs A segment setting unit that identifies an intersection of tangent lines, sets a line segment between the pair of intersections as a segment of the lane, and outputs the segment to the image processing unit, and the image processing unit includes the segment An apparatus for detecting a lane on a road surface, which is configured to update an image in which boundary lines on both sides are specified based on the image.

Images