Cubic lane boundary model
cubicLaneBoundary object contains information about a cubic
lane boundary model.
To generate cubic lane boundary models that fit a set of boundary points and an
approximate width, use the
findCubicLaneBoundaries function. If you already know your cubic
parameters, create lane boundary models by using the
function (described here).
cubicParameters— Parameters for cubic models
[A B C D]real-valued vector | matrix of
[A B C D]values
Parameters for cubic models of the form y =
Cx + D, specified as an
[A B C D] real-valued vector or as a matrix of
[A B C D] values. Each row of
cubicParameters describes a separate cubic lane
Parameters— Coefficients for cubic model
[A B C D]real-valued vector
Coefficients for a cubic model of the form y =
Cx + D, specified as an
[A B C D] real-valued vector.
BoundaryType— Type of boundary
Type of boundary, specified as a
LaneBoundaryType of supported lane
boundaries. The supported lane boundary types are:
Specify a lane boundary type as
Strength— Strength of boundary model
Strength of the boundary model, specified as a real scalar.
the ratio of the number of unique x-axis
locations on the boundary to the length of the boundary
specified by the
XExtent property. A solid
line without any breaks has a higher strength than a dotted line
that has breaks along the full length of the boundary.
XExtent— Length of boundary along x-axis
[minX maxX]real-valued vector
Length of the boundary along the x-axis, specified as a
maxX] real-valued vector that describes
the minimum and maximum x-axis
|Obtain y-coordinates of lane boundaries given x-coordinates|
Create left-lane and right-lane cubic boundary models.
llane = cubicLaneBoundary([-0.0001 0.0 0.003 1.6]); rlane = cubicLaneBoundary([-0.0001 0.0 0.003 -1.8]);
Create a bird's-eye plot and lane boundary plotter. Plot the lane boundaries.
bep = birdsEyePlot('XLimits',[0 30],'YLimits',[-10 10]); lbPlotter = laneBoundaryPlotter(bep,'DisplayName','Lane boundaries'); plotLaneBoundary(lbPlotter, [llane rlane]);
Find lanes in an image by using cubic lane boundary models. Overlay the identified lanes on the original image and on a bird's-eye-view transformation of the image.
Load an image of a road with lanes. The image was obtained from a camera sensor mounted on the front of a vehicle.
I = imread('road.png');
Transform the image into a bird's-eye-view image by using a preconfigured sensor object. This object models the sensor that captured the original image.
bevSensor = load('birdsEyeConfig'); birdsEyeImage = transformImage(bevSensor.birdsEyeConfig,I); imshow(birdsEyeImage)
Set the approximate lane marker width in world units (meters).
approxBoundaryWidth = 0.25;
Detect lane features and display them as a black-and-white image.
birdsEyeBW = segmentLaneMarkerRidge(rgb2gray(birdsEyeImage), ... bevSensor.birdsEyeConfig,approxBoundaryWidth); imshow(birdsEyeBW)
Obtain lane candidate points in world coordinates.
[imageX,imageY] = find(birdsEyeBW); xyBoundaryPoints = imageToVehicle(bevSensor.birdsEyeConfig,[imageY,imageX]);
Find lane boundaries in the image by using the
findCubicLaneBoundaries function. By default, the function returns a maximum of two lane boundaries. The boundaries are stored in an array of
boundaries = findCubicLaneBoundaries(xyBoundaryPoints,approxBoundaryWidth);
insertLaneBoundary to overlay the lanes on the original image. The
XPoints vector represents the lane points, in meters, that are within range of the ego vehicle's sensor. Specify the lanes in different colors. By default, lanes are yellow.
XPoints = 3:30; figure sensor = bevSensor.birdsEyeConfig.Sensor; lanesI = insertLaneBoundary(I,boundaries(1),sensor,XPoints); lanesI = insertLaneBoundary(lanesI,boundaries(2),sensor,XPoints,'Color','green'); imshow(lanesI)
View the lanes in the bird's-eye-view image.
figure BEconfig = bevSensor.birdsEyeConfig; lanesBEI = insertLaneBoundary(birdsEyeImage,boundaries(1),BEconfig,XPoints); lanesBEI = insertLaneBoundary(lanesBEI,boundaries(2),BEconfig,XPoints,'Color','green'); imshow(lanesBEI)