plotParkingLaneMarking

Display parking lane markings on bird’s-eye plot

Since R2021b

Syntax

plotParkingLaneMarking(lmPlotter,plmv,plmf)

Description

plotParkingLaneMarking(lmPlotter,plmv,plmf) displays parking lane marking vertices plmv and parking lane marking faces plmf on a bird's-eye plot. The lane marking plotter lmPlotter is associated with a birdsEyePlot object and configures the display of the specified lane markings.

To remove all lane markings associated with the lane marking plotter lmPlotter, use the clearData function and specify lmPlotter as the input argument.

Examples

Generate Detections of Cars in Parking Lot

Generate detections of cars parked in a parking lot, and plot the detections on a bird's-eye plot.

Create a driving scenario containing a road and parking lot.

scenario = drivingScenario;
roadcenters = [10 40; 10 -40];
road(scenario,roadcenters);
vertices = [0 20; 20 20; 20 -20; 0 -20];
parkingLot(scenario,vertices,ParkingSpace=parkingSpace);

Add an ego vehicle and specify a trajectory in which the vehicle drives through the parking lot.

ego = vehicle(scenario);
waypoints = [10 35 0; 10 10 0];
speed = 5; % m/s
smoothTrajectory(ego,waypoints,speed)

Create parked cars in several parking spaces. Plot the scenario.

parkedCar1 = vehicle(scenario,Position=[15.8 12.4 0]);
parkedCar2 = vehicle(scenario,Position=[15.8 -12.4 0]);
parkedCar3 = vehicle(scenario,Position=[2 -9.7 0]);
parkedCar4 = vehicle(scenario,Position=[2 9.7 0]);
plot(scenario)

Create a vision sensor for generating the detections. By default, the sensor is mounted to the front bumper of the ego vehicle.

sensor = visionDetectionGenerator;

Create a bird's-eye plot and plotters for visualizing the target outlines, road boundaries, parking lane markings, sensor coverage area, and detections. Then, simulate the scenario and generate the detections.

bep = birdsEyePlot(XLim=[-40 40],YLim=[-30 30]);
olPlotter = outlinePlotter(bep);
lbPlotter = laneBoundaryPlotter(bep);
lmPlotter = laneMarkingPlotter(bep,DisplayName="Parking lanes");
caPlotter = coverageAreaPlotter(bep,DisplayName="Coverage area");
detPlotter = detectionPlotter(bep,DisplayName="Detections");

while advance(scenario)

    % Plot target outlines.
    [position,yaw,length,width,originOffset,color] = targetOutlines(ego);
    plotOutline(olPlotter,position,yaw,length,width)

    % Plot lane boundaries of ego vehicle.
    rbEgo = roadBoundaries(ego);
    plotLaneBoundary(lbPlotter,rbEgo)

    % Plot parking lane markings.
    [plmv,plmf] = parkingLaneMarkingVertices(ego);
    plotParkingLaneMarking(lmPlotter,plmv,plmf)

    % Plot sensor coverage area.
    mountPosition = sensor.SensorLocation;
    range = sensor.MaxRange;
    orientation = sensor.Yaw;
    fieldOfView = sensor.FieldOfView(1);
    plotCoverageArea(caPlotter,mountPosition,range,orientation,fieldOfView)

    % Generate and plot detections.
    actors = targetPoses(ego);
    time = scenario.SimulationTime;
    [dets,isValidTime] = sensor(actors,time);
    if isValidTime
        positions = cell2mat(cellfun(@(x)([x.Measurement(1) x.Measurement(2)]), ...
            dets,UniformOutput=false));
        plotDetection(detPlotter,positions)
    end

end

Input Arguments

lmPlotter — Lane marking plotter

LaneMarkingPlotter object

Lane marking plotter, specified as a LaneMarkingPlotter object. This object is stored in the Plotters property of a birdsEyePlot object and configures the display of the specified parking lane markings in the bird's-eye plot. To create this object, use the laneMarkingPlotter function.

plmv — Parking lane marking vertices

V-by-3 real-valued matrix

Parking lane marking vertices, specified as a V-by-3 real-valued matrix. Each row of plmv represents the x, y, and z coordinates of one vertex in the lane marking. The plotter uses only the x and y coordinates. V is the number of vertices in the marking.

To obtain vertices and faces from parking lane markings in a driving scenario, use the parkingLaneMarkingVertices function.

plmf — Parking lane marking faces

matrix of integers

Parking lane marking faces, specified as a matrix of integers. Each row of plmf is a face that defines the connection between vertices for one parking lane marking. For more details, see Faces.

To obtain vertices and faces from parking lane markings in a driving scenario, use the parkingLaneMarkingVertices function.

Version History

Introduced in R2021b