ForwardCollisionWarningTrackingExample_YJ.m

%% Forward Collision Warning Using Sensor Fusion
% This example shows how to perform forward collision warning by fusing
% data from vision and radar sensors to track objects in front of the
% vehicle.
%
%   Copyright 2016 The MathWorks, Inc.

%% Overview
% Forward collision warning (FCW) is an important feature in driver
% assistance and automated driving systems, where the goal is to provide
% correct, timely, and reliable warnings to the driver before an impending
% collision with the vehicle in front. To achieve the goal, vehicles are
% equipped with forward-facing vision and radar sensors. Sensor fusion is
% required to increase the probability of accurate warnings and minimize
% the probability of false warnings.
%
% For the purposes of this example, a test car (the ego vehicle) was
% equipped with various sensors and their outputs were recorded. The
% sensors used for this example were:
%
% # Vision sensor, which provided lists of observed objects with their 
%   classification and information about lane boundaries. The object lists 
%   were reported 10 times per second. Lane boundaries were reported 20
%   times per second.
% # Radar sensor with medium and long range modes, which provided lists of 
%   unclassified observed objects. The object lists were reported 20 times 
%   per second.
% # IMU, which reported the speed and turn rate of the ego vehicle 20 times
%   per second.
% # Video camera, which recorded a video clip of the scene in front of the
%   car. Note: This video is not used by the tracker and only serves to
%   display the tracking results on video for verification.
%
% The process of providing a forward collision warning comprises the
% following steps: 
%
% # Obtain the data from the sensors.
% # Fuse the sensor data to get a list of tracks, i.e., estimated
%   positions and velocities of the objects in front of the car.
% # Issue warnings based on the tracks and FCW criteria. The FCW criteria
%   are based on the Euro NCAP AEB test procedure and take into account the
%   relative distance and relative speed to the object in front of the car.
%
% For more information about tracking multiple objects, see 
% <matlab:helpview(fullfile(docroot,'toolbox','vision','vision.map'),'multipleObjectTracking') Multiple Object Tracking>.
%
% The visualization in this example is done using
% <matlab:doc('monoCamera'), monoCamera> and <matlab:doc('birdsEyePlot')
% birdsEyePlot>. For brevity, the functions that create and update the
% display were moved to helper functions outside of this example. For more
% information on how to use these displays, see
% <UsingMonoCameraToDisplayObjectOnVideoExample.html Annotate Video Using
% Detections in Vehicle Coordinates> and <BirdsEyePlotExample.html
% Visualize Sensor Coverage, Detections, and Tracks>.
%
% This example is a script, with the main body shown here and helper
% routines in the form of
% <matlab:helpview(fullfile(docroot,'toolbox','matlab','matlab_prog','matlab_prog.map'),'matlab_prog_localfunctions_livescripts') local functions> 
% in the sections that follow.

close all

% Set up the display
[videoReader, videoDisplayHandle, bepPlotters, sensor] = helperCreateFCWDemoDisplay('01_city_c2s_fcw_10s.mp4', 'SensorConfigurationData.mat');

% Read the recorded detections file
[visionObjects, radarObjects, inertialMeasurementUnit, laneReports, ...
    timeStep, numSteps] = readSensorRecordingsFile('01_city_c2s_fcw_10s_sensor.mat');

% An initial ego lane is calculated. If the recorded lane information is
% invalid, define the lane boundaries as straight lines half a lane
% distance on each side of the car
laneWidth = 3.6; % meters
egoLane = struct('left', [0 0 laneWidth/2], 'right', [0 0 -laneWidth/2]);

% Prepare some time variables
time = 0;           % Time since the beginning of the recording
currentStep = 0;    % Current timestep
snapTime = 9.3;     % The time to capture a snapshot of the display

% Initialize the tracker
[tracker, positionSelector, velocitySelector] = setupTracker();

while currentStep < numSteps && ishghandle(videoDisplayHandle)
    % Update scenario counters
    currentStep = currentStep + 1;
    time = time + timeStep;
    
    % Process the sensor detections as objectDetection inputs to the tracker
    [detections, laneBoundaries, egoLane] = processDetections(...
        visionObjects(currentStep), radarObjects(currentStep), ...
        inertialMeasurementUnit(currentStep), laneReports(currentStep), egoLane, time);
    
    % Using the list of objectDetections, return the tracks, updated to time
    confirmedTracks = updateTracks(tracker, detections, time);
    
    % Find the most important object and calculate the forward collision
    % warning    
    mostImportantObject = findMostImportantObject(confirmedTracks, egoLane, positionSelector, velocitySelector);
    
    % Update video and birds-eye plot displays
    frame = readFrame(videoReader);     % Read video frame
    helperUpdateFCWDemoDisplay(frame, videoDisplayHandle, bepPlotters, ...
        laneBoundaries, sensor, confirmedTracks, mostImportantObject, positionSelector, ...
        velocitySelector, visionObjects(currentStep), radarObjects(currentStep));
    
    % Capture a snapshot
    if time >= snapTime && time < snapTime + timeStep
        snapnow;
    end
end
displayEndOfDemoMessage(mfilename)
%% Create the Multi-Object Tracker
% The |multiObjectTracker| tracks the objects around the ego vehicle based
% on the object lists reported by the vision and radar sensors. By fusing
% information from both sensors, the probabilty of a false collision
% warning is reduced.
%
% The |setupTracker| function returns the |multiObjectTracker|. 
% When creating a |multiObjectTracker|, consider the following: 
%
% # |FilterInitializationFcn|: The likely motion and measurement models.
%   In this case, the objects are expected to have a constant acceleration
%   motion[CA model]. Although you can configure a linear Kalman filter for this
%   model, |initConstantAccelerationFilter| configures an extended Kalman
%   filter. See the 'Define a Kalman filter' section.
% # |AssignmentThreshold|: How far detections can fall from tracks. 
%   The default value for this parameter is 30. If there are detections
%   that are not assigned to tracks, but should be, increase this value. If
%   there are detections that get assigned to tracks that are too far,
%   decrease this value. This example uses 35.
% # |NumCoastingUpdates|: How many times a track is coasted before deletion.
%   Coasting is a term used for updating the track without an assigned
%   detection (predicting).
%   The default value for this parameter is 5. In this case, the tracker is
%   called 20 times a second and there are two sensors, so there is no need
%   to modify the default.
% # |ConfirmationParameters|: The parameters for confirming a track.
%   A new track is initialized with every unassigned detection. Some of
%   these detections might be false, so all the tracks are initialized as
%   |'Tentative'|. To confirm a track, it has to be detected at least _M_
%   times in _N_ tracker updates. The choice of _M_ and _N_ depends on the
%   visibility of the objects. This example uses the default of 2
%   detections out of 3 updates.
%
% The outputs of |setupTracker| are:
%
% * |tracker| - The |multiObjectTracker| that is configured for this case.
% * |positionSelector| - A matrix that specifies which elements of the
%   State vector are the position: |position = positionSelector * State|
% * |velocitySelector| - A matrix that specifies which elements of the 
%   State vector are the velocity: |velocity = velocitySelector * State|
    function [tracker, positionSelector, velocitySelector] = setupTracker()
        tracker = multiObjectTracker('FilterInitializationFcn', @initConstantAccelerationFilter, ...
            'AssignmentThreshold', 35, 'ConfirmationParameters', [2 3], 'NumCoastingUpdates', 5);
        
        % The State vector is:
        %   In constant velocity:     State = [x;vx;y;vy]
        %   In constant acceleration: State = [x;vx;ax;y;vy;ay]
                
        % Define which part of the State is the position. For example:
        %   In constant velocity:     [x;y] = [1 0 0 0; 0 0 1 0] * State
        %   In constant acceleration: [x;y] = [1 0 0 0 0 0; 0 0 0 1 0 0] * State
        positionSelector = [1 0 0 0 0 0; 0 0 0 1 0 0];
        
        % Define which part of the State is the velocity. For example:
        %   In constant velocity:     [x;y] = [0 1 0 0; 0 0 0 1] * State
        %   In constant acceleration: [x;y] = [0 1 0 0 0 0; 0 0 0 0 1 0] * State
        velocitySelector = [0 1 0 0 0 0; 0 0 0 0 1 0];
    end

%% Define a Kalman Filter
% The |multiObjectTracker| defined in the previous section uses the filter
% initialization function defined in this section to create a Kalman filter
% (linear, extended, or unscented). This filter is then used for tracking
% each object around the ego vehicle.
function filter = initConstantAccelerationFilter(detection)
% This function shows how to configure a constant acceleration filter. The
% input is an objectDetection and the output is a tracking filter.
% For clarity, this function shows how to configure a trackingKF,
% trackingEKF, or trackingUKF for constant acceleration.
%
% Steps for creating a filter:
%   1. Define the motion model and state
%   2. Define the process noise
%   3. Define the measurement model
%   4. Initialize the state vector based on the measurement
%   5. Initialize the state covariance based on the measurement noise
%   6. Create the correct filter

    % Step 1: Define the motion model and state
    % This example uses a constant acceleration model, so:
    STF = @constacc;     % State-transition function, for EKF and UKF
    STFJ = @constaccjac; % State-transition function Jacobian, only for EKF
    % The motion model implies that the state is [x;vx;ax;y;vy;ay]
    % You can also use constvel and constveljac to set up a constant
    % velocity model, constturn and constturnjac to set up a constant turn
    % rate model, or write your own models.

    % Step 2: Define the process noise
    dt = 0.05; % Known timestep size
    sigma = 1; % Magnitude of the unknown acceleration change rate
    % The process noise along one dimension
    Q1d = [dt^4/4, dt^3/2, dt^2/2; dt^3/2, dt^2, dt; dt^2/2, dt, 1] * sigma^2;
    Q = blkdiag(Q1d, Q1d); % 2-D process noise

    % Step 3: Define the measurement model    
    MF = @fcwmeas;       % Measurement function, for EKF and UKF
    MJF = @fcwmeasjac;   % Measurement Jacobian function, only for EKF

    % Step 4: Initialize a state vector based on the measurement 
    % The sensors measure [x;vx;y;vy] and the constant acceleration model's
    % state is [x;vx;ax;y;vy;ay], so the third and sixth elements of the
    % state vector are initialized to zero.
    state = [detection.Measurement(1); detection.Measurement(2); 0; detection.Measurement(3); detection.Measurement(4); 0];

    % Step 5: Initialize the state covariance based on the measurement
    % noise. The parts of the state that are not directly measured are
    % assigned a large measurement noise value to account for that.
    L = 100; % A large number relative to the measurement noise
    stateCov = blkdiag(detection.MeasurementNoise(1:2,1:2), L, detection.MeasurementNoise(3:4,3:4), L);

    % Step 6: Create the correct filter. 
    % Use 'KF' for trackingKF, 'EKF' for trackingEKF, or 'UKF' for trackingUKF
    FilterType = 'EKF';

    % Creating the filter:
    switch FilterType
        case 'EKF'
            filter = trackingEKF(STF, MF, state,...
                'StateCovariance', stateCov, ...
                'MeasurementNoise', detection.MeasurementNoise(1:4,1:4), ...
                'StateTransitionJacobianFcn', STFJ, ...
                'MeasurementJacobianFcn', MJF, ...
                'ProcessNoise', Q ...            
                );
        case 'UKF'
            filter = trackingUKF(STF, MF, state, ...
                'StateCovariance', stateCov, ...
                'MeasurementNoise', detection.MeasurementNoise(1:4,1:4), ...
                'Alpha', 1e-1, ...            
                'ProcessNoise', Q ...
                );
        case 'KF' % The ConstantAcceleration model is linear and KF can be used                
            % Define the measurement model: measurement = H * state
            % In this case:
            %   measurement = [x;vx;y;vy] = H * [x;vx;ax;y;vy;ay]
            % So, H = [1 0 0 0 0 0; 0 1 0 0 0 0; 0 0 0 1 0 0; 0 0 0 0 1 0]
            %
            % Note that ProcessNoise is automatically calculated by the
            % ConstantAcceleration motion model
            H = [1 0 0 0 0 0; 0 1 0 0 0 0; 0 0 0 1 0 0; 0 0 0 0 1 0];
            filter = trackingKF('MotionModel', '2D Constant Acceleration', ...
                'MeasurementModel', H, 'State', state, ...
                'MeasurementNoise', detection.MeasurementNoise(1:4,1:4), ...
                'StateCovariance', stateCov);
    end
end

%%% Process and Format the Detections
% The recorded information must be processed and formatted before it can be
% used by the tracker. This has the following steps:
%
% # Cleaning the radar detections from unnecessary clutter detections. The
%   radar reports many objects that correspond to fixed objects, which
%   include: guard-rails, the road median, traffic signs, etc. If these
%   detections are used in the tracking, they create false tracks of fixed
%   objects at the edges of the road and therefore must be removed before
%   calling the tracker. Radar objects are considered nonclutter if they
%   are either stationary in front of the car or moving in its vicinity.
% # Formatting the detections as input to the tracker, i.e., an array of
%   <matlab:doc('objectDetection'), objectDetection> elements. See the
%   |processVideo| and |processRadar| supporting functions at the end of
%   this example.
    function [detections,laneBoundaries, egoLane] = processDetections(visionFrame, radarFrame, IMUFrame, laneFrame, egoLane, time)
        % Inputs: 
        %   visionFrame - objects reported by the vision sensor for this time frame
        %   radarFrame  - objects reported by the radar sensor for this time frame
        %   IMUFrame    - inertial measurement unit data for this time frame
        %   laneFrame   - lane reports for this time frame
        %   egoLane     - the estimated ego lane
        %   time        - the time corresponding to the time frame
        
        % Remove clutter radar objects
        [laneBoundaries, egoLane] = processLanes(laneFrame, egoLane);
        % clutter:ÔÓÂÒ
        realRadarObjects = findNonClutterRadarObjects(radarFrame.object, radarFrame.numObjects, IMUFrame.velocity, laneBoundaries);        
        
        % Return an empty list if no objects are reported
        
        % Counting the total number of object        
        detections = {};
        if (visionFrame.numObjects + numel(realRadarObjects)) == 0
            return;
        end
                        
        % Process the remaining radar objects
        detections = processRadar(detections, realRadarObjects, time);    
        
        % Process video objects
        detections = processVideo(detections, visionFrame, time);
    end
%% Update the Tracker
% To update the tracker, call the |updateTracks| method with the following
% inputs:
%
% # |tracker| - The |multiObjectTracker| that was configured earlier. See
%   the 'Create the Multi-Object Tracker' section.
% # |detections| - A list of |objectDetection| objects that was created by
%   |processDetections|
% # |time| - The current scenario time. 
%
% The output from the tracker is a |struct| array of tracks. 

%%% Find the Most Important Object and Issue a Forward Collision Warning
% The most important object (MIO) is defined as the track that is in the
% ego lane and is closest in front of the car, i.e., with the smallest
% positive _x_ value. To lower the probability of false alarms, only
% confirmed tracks are considered.
%
% Once the MIO is found, the relative speed between the car and MIO is
% calculated. The relative distance and relative speed determine the
% forward collision warning. There are 3 cases of FCW:
%
% # Safe (green): There is no car in the ego lane (no MIO), the MIO is 
%   moving away from the car, or the distance is maintained constant.
% # Caution (yellow): The MIO is moving closer to the car, but is still at
%   a distance above the FCW distance. FCW distance is calculated using the
%   Euro NCAP AEB Test Protocol. Note that this distance varies with the
%   relative speed between the MIO and the car, and is greater when the
%   closing speed is higher.
% # Warn (red): The MIO is moving closer to the car, and its distance is 
%   less than the FCW distance.
%
% Euro NCAP AEB Test Protocol defines the following distance calculation:
%%
% $d_{FCW} = 1.2 * v_{rel} + \frac{v_{rel}^2}{2a_{max}}$
%
% where:
%
% $d_{FCW}$ is the forward collision warning distance.
%
% $v_{rel}$ is the relative velocity between the two vehicles.
%
% $a_{max}$ is the maximum deceleration, defined to be 40% of the gravity
% acceleration.
    function mostImportantObject = findMostImportantObject(confirmedTracks,egoLane,positionSelector,velocitySelector)        
        
        % Initialize outputs and parameters
        MIO = [];               % By default, there is no MIO
        trackID = [];           % By default, there is no trackID associated with an MIO
        FCW = 3;                % By default, if there is no MIO, then FCW is 'safe'
        threatColor = 'green';  % By default, the threat color is green
        maxX = 1000;  % Far enough forward so that no track is expected to exceed this distance
        gAccel = 9.8; % Constant gravity acceleration, in m/s^2
        maxDeceleration = 0.4 * gAccel; % Euro NCAP AEB definition
        delayTime = 1.2; % Delay time for a driver before starting to break, in seconds
        
        positions = getTrackPositions(confirmedTracks, positionSelector);
        velocities = getTrackVelocities(confirmedTracks, velocitySelector);
        
        for i = 1:numel(confirmedTracks)            
            x = positions(i,1);
            y = positions(i,2);
                        
            relSpeed = velocities(i,1); % The relative speed between the cars, along the lane
            
            if x < maxX && x > 0 % No point checking otherwise
                yleftLane  = polyval(egoLane.left,  x);
                yrightLane = polyval(egoLane.right, x);
                if (yrightLane <= y) && (y <= yleftLane)
                    maxX = x;
                    trackID = i;
                    MIO = confirmedTracks(i).TrackID;
                    if relSpeed < 0 % Relative speed indicates object is getting closer
                        % Calculate expected braking distance according to
                        % Euro NCAP AEB Test Protocol                        
                        d = abs(relSpeed) * delayTime + relSpeed^2 / 2 / maxDeceleration;
                        if x <= d % 'warn'
                            FCW = 1;
                            threatColor = 'red';
                        else % 'caution'
                            FCW = 2;
                            threatColor = 'yellow';
                        end
                    end
                end
            end
        end
        mostImportantObject = struct('ObjectID', MIO, 'TrackIndex', trackID, 'Warning', FCW, 'ThreatColor', threatColor);
    end

%% Summary
% This example showed how to create a forward collision warning system for
% a vehicle equipped with vision, radar, and IMU sensors. It used
% |objectDetection| objects to pass the sensor reports to the
% |multiObjectTracker| object that fused them and tracked objects in front
% of the ego car.
%
% Try using different parameters for the tracker to see how they affect the
% tracking quality. Try modifying the tracking filter to use |trackingKF| 
% or |trackingUKF|, or to define a different motion model, e.g., constant
% velocity or constant turn. Finally, you can try to define your own motion
% model.

%% Supporting Functions
%%%
% *readSensorRecordingsFile*
% Reads recorded sensor data from a file
function [visionObjects, radarObjects, inertialMeasurementUnit, laneReports, ...
    timeStep, numSteps] = readSensorRecordingsFile(sensorRecordingFileName)
% Read Sensor Recordings
% The |ReadDetectionsFile| function reads the recorded sensor data file.
% The recorded data is a single structure that is divided into the
% following substructures:
%
% # |inertialMeasurementUnit|, a struct array with fields: timeStamp,
%   velocity, and yawRate. Each element of the array corresponds to a
%   different timestep.
% # |laneReports|, a struct array with fields: left and right. Each element
%   of the array corresponds to a different timestep.
%   Both left and right are structures with fields: isValid, confidence,
%   boundaryType, offset, headingAngle, and curvature.
% # |radarObjects|, a struct array with fields: timeStamp (see below),
%   numObjects (integer) and object (struct). Each element of the array
%   corresponds to a different timestep.
%   |object| is a struct array, where each element is a separate object,
%   with the fields: id, status, position(x;y;z), velocity(vx,vy,vz),
%   amplitude, and rangeMode.
%   Note: z is always constant and vz=0.
% # |visionObjects|, a struct array with fields: timeStamp (see below), 
%   numObjects (integer) and object (struct). Each element of the array 
%   corresponds to a different timestep.
%   |object| is a struct array, where each element is a separate object,
%   with the fields: id, classification, position (x;y;z),
%   velocity(vx;vy;vz), size(dx;dy;dz). Note: z=vy=vz=dx=dz=0
%
% The timeStamp for recorded vision and radar objects is a uint64 variable
% holding microseconds since the Unix epoch. Timestamps are recorded about
% 50 milliseconds apart. There is a complete synchronization between the
% recordings of vision and radar detections, therefore the timestamps are
% not used in further calculations.

A = load(sensorRecordingFileName);
visionObjects = A.vision;
radarObjects = A.radar;
laneReports = A.lane;
inertialMeasurementUnit = A.inertialMeasurementUnit;

timeStep = 0.05;                 % Data is provided every 50 milliseconds
numSteps = numel(visionObjects); % Number of recorded timesteps
end
%%%
% *processLanes* 
% Converts sensor-reported lanes to |parabolicLaneBoundary| lanes and
% maintans a persistent ego lane estimate
function [laneBoundaries, egoLane] = processLanes(laneReports, egoLane)
    % Lane boundaries are updated based on the laneReports from the recordings.
    % Since some laneReports contain invalid (isValid = false) reports or
    % impossible parameter values (-1e9), these lane reports are ignored and
    % the previous lane boundary is used.
    leftLane = laneReports.left;
    rightLane = laneReports.right;

    % Check the validity of the reported left lane
    cond = (leftLane.isValid && leftLane.confidence) && ...
        ~(leftLane.headingAngle == -1e9 || leftLane.curvature == -1e9);
    if cond
        egoLane.left = cast([leftLane.curvature, leftLane.headingAngle, leftLane.offset], 'double');
    end

    % Update the left lane boundary parameters or use the previous ones
    leftParams  = egoLane.left;
    leftBoundaries = parabolicLaneBoundary(leftParams);
    leftBoundaries.Strength = 1;

    % Check the validity of the reported right lane
    cond = (rightLane.isValid && rightLane.confidence) && ...
        ~(rightLane.headingAngle == -1e9 || rightLane.curvature == -1e9);
    if cond
        egoLane.right  = cast([rightLane.curvature, rightLane.headingAngle, rightLane.offset], 'double');
    end

    % Update the right lane boundary parameters or use the previous ones
    rightParams = egoLane.right;
    rightBoundaries = parabolicLaneBoundary(rightParams);
    rightBoundaries.Strength = 1;

    laneBoundaries = [leftBoundaries, rightBoundaries];
end
%%%
% *findNonClutterRadarObjects*
% Removes radar objects that are considered part of the clutter
function realRadarObjects = findNonClutterRadarObjects(radarObject, numRadarObjects, egoSpeed, laneBoundaries)
    % The radar objects include many objects that belong to the clutter.
    % Clutter is defined as a stationary object that is not in front of the
    % car. The following types of objects pass as nonclutter:
    %   
    % # Any object in front of the car
    % # Any moving object in the area of interest around the car, including
    %   objects that move at a lateral speed around the car
    
    % Allocate memory
    normVs = zeros(numRadarObjects, 1);
    inLane = zeros(numRadarObjects, 1);
    inZone = zeros(numRadarObjects, 1);
    
    % Parameters
    LaneWidth = 3.6;            % What is considered in front of the car
    ZoneWidth = 1.7*LaneWidth;  % A wider area of interest
    minV = 1;                   % Any object that moves slower than minV is considered stationary
    for j = 1:numRadarObjects
        [vx, vy] = calculateGroundSpeed(radarObject(j).velocity(1),radarObject(j).velocity(2),egoSpeed);
        normVs(j) = norm([vx,vy]);
        laneBoundariesAtObject = computeBoundaryModel(laneBoundaries, radarObject(j).position(1));
        laneCenter = mean(laneBoundariesAtObject);
        inLane(j) = (abs(radarObject(j).position(2) - laneCenter) <= LaneWidth/2);
        inZone(j) = (abs(radarObject(j).position(2) - laneCenter) <= max(abs(vy)*2, ZoneWidth));
    end         
    realRadarObjectsIdx = union(...
        intersect(find(normVs > minV), find(inZone == 1)), find(inLane == 1));
        
    realRadarObjects = radarObject(realRadarObjectsIdx);
end
%%%
% *calculateGroundSpeed*
% Calculates the true ground speed of a radar-reported object from the
% relative speed and the ego car speed
function [Vx,Vy] = calculateGroundSpeed(Vxi,Vyi,egoSpeed)
% Inputs
    %   (Vxi,Vyi) : relative object speed
    %   egoSpeed  : ego vehicle speed
    % Outputs
    %   [Vx,Vy]   : ground object speed

    Vx = Vxi + egoSpeed;    % Calculate longitudinal ground speed
    theta = atan2(Vyi,Vxi); % Calculate heading angle
    Vy = Vx * tan(theta);   % Calculate lateral ground speed

end
%%%
% *processVideo*
% Converts reported vision objects to a list of |objectDetection| objects
function postProcessedDetections = processVideo(postProcessedDetections, visionFrame, t)
% Process the video objects into objectDetection objects
numRadarObjects = numel(postProcessedDetections);
numVisionObjects = visionFrame.numObjects;
if numVisionObjects
    classToUse = class(visionFrame.object(1).position);
    visionMeasCov = cast(diag([2,2,2,100]), classToUse);
    % Process Vision Objects:
    for i=1:numVisionObjects
        object = visionFrame.object(i);
        postProcessedDetections{numRadarObjects+i} = objectDetection(t,...
            [object.position(1); object.velocity(1); object.position(2); 0], ...
            'SensorIndex', 1, 'MeasurementNoise', visionMeasCov, ...
            'MeasurementParameters', {1}, ...
            'ObjectClassID', object.classification, ...
            'ObjectAttributes', {object.id, object.size});
    end
end
end
%%%
% *processRadar*
% Converts reported radar objects to a list of |objectDetection| objects
function postProcessedDetections = processRadar(postProcessedDetections, realRadarObjects, t)
% Process the radar objects into objectDetection objects
numRadarObjects = numel(realRadarObjects);
if numRadarObjects
    classToUse = class(realRadarObjects(1).position);
    radarMeasCov = cast(diag([2,2,2,100]), classToUse);
    % Process Radar Objects:
    for i=1:numRadarObjects
        object = realRadarObjects(i);
        postProcessedDetections{i} = objectDetection(t, ...
            [object.position(1); object.velocity(1); object.position(2); object.velocity(2)], ...
            'SensorIndex', 2, 'MeasurementNoise', radarMeasCov, ...
            'MeasurementParameters', {2}, ...
            'ObjectAttributes', {object.id, object.status, object.amplitude, object.rangeMode});
    end
end
end
%%%
% *fcwmeas*
% The measurement function used in this forward collision warning example
function measurement = fcwmeas(state, sensorID)
% The example measurements depend on the sensor type, which is reported by
% the MeasurementParameters property of the objectDetection. The following
% two sensorID values are used:
%   sensorID=1: video objects, the measurement is [x;vx;y]. 
%   sensorID=2: radar objects, the measurement is [x;vx;y;vy]. 
% The state is:
%   Constant velocity       state = [x;vx;y;vy] 
%   Constant turn           state = [x;vx;y;vy;omega]
%   Constant acceleration   state = [x;vx;ax;y;vy;ay]

    if numel(state) < 6 % Constant turn or constant velocity
        switch sensorID
            case 1 % video
                measurement = [state(1:3); 0];
            case 2 % radar
                measurement = state(1:4);
        end
    else % Constant acceleration
        switch sensorID
            case 1 % video
                measurement = [state(1:2); state(4); 0];
            case 2 % radar
                measurement = [state(1:2); state(4:5)];
        end
    end
end
%%%
% *fcwmeasjac*
% The Jacobian of the measurement function used in this forward collision
% warning example
function jacobian = fcwmeasjac(state, sensorID)
% The example measurements depend on the sensor type, which is reported by
% the MeasurementParameters property of the objectDetection. We choose
% sensorID=1 for video objects and sensorID=2 for radar objects.  The
% following two sensorID values are used:
%   sensorID=1: video objects, the measurement is [x;vx;y]. 
%   sensorID=2: radar objects, the measurement is [x;vx;y;vy]. 
% The state is:
%   Constant velocity       state = [x;vx;y;vy] 
%   Constant turn           state = [x;vx;y;vy;omega]
%   Constant acceleration   state = [x;vx;ax;y;vy;ay]

    numStates = numel(state);
    jacobian = zeros(4, numStates);

    if numel(state) < 6 % Constant turn or constant velocity
        switch sensorID
            case 1 % video
                jacobian(1,1) = 1;
                jacobian(2,2) = 1;
                jacobian(3,3) = 1;
            case 2 % radar
                jacobian(1,1) = 1;
                jacobian(2,2) = 1;
                jacobian(3,3) = 1;
                jacobian(4,4) = 1;
        end
    else % Constant acceleration
        switch sensorID
            case 1 % video
                jacobian(1,1) = 1;
                jacobian(2,2) = 1;
                jacobian(3,4) = 1;
            case 2 % radar
                jacobian(1,1) = 1;
                jacobian(2,2) = 1;
                jacobian(3,4) = 1;
                jacobian(4,5) = 1;
        end
    end
end