laneBoundaryTracker
Description
The laneBoundaryTracker
System object™ tracks lane boundary detections. It can track multiple lane boundary detections
in the form of parabolicLaneBoundary, cubicLaneBoundary,
and clothoidLaneBoundary objects.
A lane boundary tracker confirms a track when the laneBoundaryTracker
System object has enough evidence to determine that the track is an actual lane and its status
is set to confirmed. For more information, see Confirmation
Threshold property.
You can initialize and configure the tracker with an external Sensor Fusion and Tracking Toolbox™ tracker. Additionally, you can customize the preprocessing and postprocessing
functions by using the PreProcessingFcn and
PostProcessingFcn properties, respectively.
To use laneBoundaryTracker:
Create the
laneBoundaryTrackerobject and set its properties.Call the object with arguments, as if it were a function.
To learn more about how System objects work, see What Are System Objects?
Note
This object requires the Scenario Builder for Automated Driving Toolbox™ support package and Sensor Fusion and Tracking Toolbox. You can install Scenario Builder for Automated Driving Toolbox from the Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.
Creation
Syntax
Description
tracker = laneBoundaryTrackerlaneBoundaryTracker
System object, tracker, with default property values.
tracker = laneBoundaryTracker(customTracker)laneBoundaryTracker
System object, tracker, initialized with an external Sensor Fusion and Tracking Toolbox tracker, customTracker.
tracker = laneBoundaryTracker(Name=Value)laneBoundaryTracker object using one or more
name-value arguments. For example, AssignmentThreshold=2 creates a
laneBoundaryTracker object with an
AssignmentThreshold value of 2.
Input Arguments
Properties
Usage
Syntax
Description
confirmedTracks = tracker(detections,time)time.
confirmedTracks = tracker(detections,time,varargin)
confirmedTracks = tracker(___,Name=Value)ShowProgress=true displays a progress bar when using the tracker
object.
Note
You can run the tracker in offline mode. For more information on offline mode
tracking, see the detections, time and
confirmedTracks argument descriptions.
Input Arguments
Name-Value Arguments
Output Arguments
Object Functions
To use an object function, specify the
System object as the first input argument. For
example, to release system resources of a System object named obj, use
this syntax:
release(obj)
Examples
Model lane boundary detections using parabolicLaneBoundary objects.
llane=parabolicLaneBoundary([-0.001 0.01 0.5]); rlane=parabolicLaneBoundary([-0.001 0.01 -0.5]);
Specify the size of the time steps.
dt=0.1;
Initialize a laneBoundaryTracker for tracking parabolicLaneBoundary objects.
tracker=laneBoundaryTracker(LaneBoundaryModel="Parabolic");Update the tracker with the lane detections.
for i=1:2 laneTracks=tracker({llane,rlane},i*dt) end
laneTracks = 0×1 empty cell array
laneTracks=2×1 cell array
{1×1 struct}
{1×1 struct}
Visualize the tracked lane boundaries.
bep=birdsEyePlot(XLimits=[0 30],YLimits=[-5 5]);
lbPlotter=laneBoundaryPlotter(bep,DisplayName="Laneboundaries");
plotLaneBoundary(lbPlotter,[laneTracks{1}.LaneBoundary laneTracks{2}.LaneBoundary])
Specify the number of steps across which to track lane boundaries in offline mode.
numFrames = 10;
Initialize a cell array and numeric array for storing lane detections and time for offline processing, respectively.
laneDets = cell(1,numFrames); time = zeros(1,numFrames);
Define the sample time.
dt = 0.1;
Create a sequence of lane boundary detections using parabolicLaneBoundary objects. To use these detections with the laneBoundaryTracker object for offline processing, enclose the detections for each time step in a single cell of the cell array.
for i = 1:numFrames a = -0.0015 - 0.0005*i; b = 0.01; c = 0.5; llane = parabolicLaneBoundary([a b c]); rlane = parabolicLaneBoundary([a b -c]); laneDets{i}= [llane, rlane]; time(i) = dt*i; end
Initialize a lane boundary tracker.
tracker = laneBoundaryTracker(AssignmentThreshold=2,MeasurementNoise=5e-3);
Track and smooth the lane boundaries in offline mode.
laneTracks = tracker(laneDets,time,UseSmoother=true);
Create a bird's-eye plot and lane boundary plotter.
bep = birdsEyePlot(XLimits=[0 30],YLimits=[-5 5]); lbDetPlotter = laneBoundaryPlotter(bep,DisplayName="Lane boundary detections"); lbTrackPlotter = laneBoundaryPlotter(bep,DisplayName="Lane boundary tracks",LineStyle="--",Color="b"); tIds = {text(0,0,''),text(0,0,'')};
Plot the tracked lane boundaries and lane tracks.
for i = 1:numFrames plotLaneBoundary(lbDetPlotter,laneDets{i}) if ~isempty(laneTracks{i}) for j = 1:numel(laneTracks{i}) boundaries(j) = laneTracks{i}{j}.LaneBoundary; tIds{j}.String = laneTracks{i}{j}.TrackID; tIds{j}.Position = [25 computeBoundaryModel(laneTracks{i}{j}.LaneBoundary,25)]; end plotLaneBoundary(lbTrackPlotter,boundaries) end drawnow pause(0.2) end
Algorithms
The laneBoundaryTracker
System object uses a combination of the joint probabilistic data association tracker and
extended Kalman filter. For more information on the joint probabilistic data association
tracker, see the Algorithms (Sensor Fusion and Tracking Toolbox) section of the
trackerJPDA
System object. For more information on the extended Kalman filter, see the Algorithms (Sensor Fusion and Tracking Toolbox) section
of the trackingEKF object.
Version History
Introduced in R2023aSee Also
parabolicLaneBoundary | cubicLaneBoundary | clothoidLaneBoundary | multiObjectTracker | trackerGNN (Sensor Fusion and Tracking Toolbox) | trackerJPDA (Sensor Fusion and Tracking Toolbox) | trackerTOMHT (Sensor Fusion and Tracking Toolbox)
