Parking lane marking vertices and faces in driving scenario
returns the parking lane marking vertices
plmf] = parkingLaneMarkingVertices(
plmv and parking lane marking
plmf contained in driving scenario
plmv outputs are in the world
scenario. Use parking lane marking vertices and faces to
display lane markings using the
laneMarkingPlotter function with a bird's-eye plot.
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
plmv— Parking lane marking vertices
Parking lane marking vertices, returned as a V-by-3 real-valued matrix. Each row of the matrix represents the (x, y, z) coordinates of a vertex. V is the number of vertices in the marking.
plmf— Parking lane marking faces
Parking lane marking faces, returned as a matrix of integers. Each row of the matrix contains the vertex connections that define a face for one lane marking. For more details, see Faces.
This function uses the
patch function to define lane marking vertices