% Create trackers for each vehicle
v1Tracker = trackerJPDA('TrackerIndex',1, 'DeletionThreshold', [4 4]); % Vehicle 1 tracker
v2Tracker = trackerJPDA('TrackerIndex',2, 'DeletionThreshold', [4 4]); % Vehicle 2 tracker
posSelector = [1 0 0 0 0 0; 0 0 1 0 0 0];

% Define sources for each vehicle
v1TrackerConfiguration = fuserSourceConfiguration('SourceIndex',1,'IsInternalSource',true, ...   % v1Tracker is internal to v1Fuser
    "CentralToLocalTransformFcn", @scenarioToEgo, 'LocalToCentralTransformFcn', @egoToScenario); % Coordinate transformation
v2FuserConfiguration = fuserSourceConfiguration('SourceIndex',4,'IsInternalSource',false);       % v2Fuser is external to v2Fuser
v1Sources = {v1TrackerConfiguration; v2FuserConfiguration};
v2TrackerConfiguration = fuserSourceConfiguration('SourceIndex',2,'IsInternalSource',true, ...   % v2Tracker is internal to v2Fuser
    "CentralToLocalTransformFcn", @scenarioToEgo, 'LocalToCentralTransformFcn', @egoToScenario); % Coordinate transformation
v2FuserConfiguration = fuserSourceConfiguration('SourceIndex',3,'IsInternalSource',false);       % v1Fuser is external to v2Fuser
v2Sources = {v2TrackerConfiguration; v2FuserConfiguration};

stateParams = struct('Frame','Rectangular','Position',[0 0 0],'Velocity',[0 0 0]);
v1Fuser = trackFuser('FuserIndex',3,...
    'MaxNumSources',2,'SourceConfigurations',v1Sources,...
    'StateFusion','Intersection','DeletionThreshold',[3 3],...
    'StateParameters',stateParams);
v2Fuser = trackFuser('FuserIndex',4,...
    'MaxNumSources',2,'SourceConfigurations',v2Sources,'StateFusion',...
    'Intersection','DeletionThreshold',[3 3],...
    'StateParameters',stateParams);

% Initialize the following variables
fusedTracks1 = objectTrack.empty(0,1);
fusedTracks2 = objectTrack.empty(0,1);
wasFuser1Updated = false;
wasFuser2Updated = false;

% Create the drivingScenario object and the two vehicles
[scenario, vehicle1, vehicle2] = createDrivingScenario;

% Create all the sensors
[sensors, numSensors, attachedVehicle] = createSensors(scenario);

% Create display
[f,plotters] = createT2TDisplay(scenario, sensors, attachedVehicle);

% Define each vehicle as a vehicle, sensors, a tracker, and plotters
v1 = struct('Actor', {vehicle1}, 'Sensors', {sensors(attachedVehicle==1)}, 'Tracker', {v1Tracker}, 'DetPlotter', {plotters.veh1DetPlotter}, 'TrkPlotter', {plotters.veh1TrkPlotter});
v2 = struct('Actor', {vehicle2}, 'Sensors', {sensors(attachedVehicle==2)}, 'Tracker', {v2Tracker}, 'DetPlotter', {plotters.veh2DetPlotter}, 'TrkPlotter', {plotters.veh2TrkPlotter});

running = true;

% For repeatable results, set the random number seed
s = rng;
rng(2019)
snaptimes = [0.5, 2.6, 4.4, 6.3, inf];
snaps = cell(numel(snaptimes,1));
i = 1;
f.Visible = 'on';
while running && ishghandle(f)
    time  = scenario.SimulationTime;
    
    % Detect and track at the vehicle level
    [tracks1,wasTracker1Updated] = detectAndTrack(v1,time,posSelector);
    [tracks2,wasTracker2Updated] = detectAndTrack(v2,time,posSelector);
    
    % Keep the tracks from the previous fuser update
    oldFusedTracks1 = fusedTracks1;
    oldFusedTracks2 = fusedTracks2;
    
    % Update the fusers
    if wasTracker1Updated || wasFuser2Updated
        tracksToFuse1 = [tracks1;oldFusedTracks2];
        if isLocked(v1Fuser) || ~isempty(tracksToFuse1)
            [fusedTracks1,~,~,info1] = v1Fuser(tracksToFuse1,time);
            wasFuser1Updated = true;
            pos = getTrackPositions(fusedTracks1,posSelector);
            plotTrack(plotters.veh1FusePlotter,pos);
        else
            wasFuser1Updated = false;
            fusedTracks1 = objectTrack.empty(0,1);
        end
    else
        wasFuser1Updated = false;
        fusedTracks1 = objectTrack.empty(0,1);
    end
    if wasTracker2Updated || wasFuser1Updated
        tracksToFuse2 = [tracks2;oldFusedTracks1];
        if isLocked(v2Fuser) || ~isempty(tracksToFuse2)
            [fusedTracks2,~,~,info2] = v2Fuser(tracksToFuse2,time);
            wasFuser2Updated = true;
            pos = getTrackPositions(fusedTracks2,posSelector);
            ids = string([fusedTracks2.TrackID]');
            plotTrack(plotters.veh2FusePlotter,pos,ids);
        else
            wasFuser2Updated = false;
            fusedTracks2 = objectTrack.empty(0,1);
        end
    else
        wasFuser2Updated = false;
        fusedTracks2 = objectTrack.empty(0,1);
    end
    
    % Update the display
    updateT2TDisplay(plotters, scenario, sensors, attachedVehicle)
    
    % Advance the scenario one time step and exit the loop if the scenario is complete
    running = advance(scenario);
    
    % Snap a shot at required times
    if time >= snaptimes(i)
        snaps{i} = getframe(f);
        i = i + 1;
    end
end

showsnap(snaps, 1)

showsnap(snaps, 2)

showsnap(snaps, 3)

showsnap(snaps, 4)

% Restart the driving scenario to return the actors to their initial positions.
restart(scenario);

% Release all the sensor objects so they can be used again.
for sensorIndex = 1:numSensors
    release(sensors{sensorIndex});
end

% Return the random seed to its previous value
rng(s)

function [scenario, egoVehicle, secondVehicle] = createDrivingScenario
% createDrivingScenario Returns the drivingScenario defined in the Designer

% Construct a drivingScenario object.
scenario = drivingScenario('SampleTime', 0.05);

% Add all road segments
roadCenters = [50.8 0.5 0; 253.4 1.5 0];
roadWidth = 12;
road(scenario, roadCenters, roadWidth);

roadCenters = [100.7 -100.6 0; 100.7 103.7 0];
road(scenario, roadCenters);

roadCenters = [201.1 -99.2 0; 199.7 99.5 0];
road(scenario, roadCenters);

% Add the ego vehicle
egoVehicle = vehicle(scenario, 'ClassID', 1, 'Position', [65.1 -0.9 0]);
waypoints = [71 -0.5 0; 148.7 -0.5 0];
speed = 12;
trajectory(egoVehicle, waypoints, speed);

% Add the second vehicle
secondVehicle = vehicle(scenario, 'ClassID', 1, 'Position', [55.1 -0.9 0]);
waypoints = [61 -0.5 0; 138.7 -0.5 0];
speed = 12;
trajectory(secondVehicle, waypoints, speed);

% Add the parked cars
vehicle(scenario, 'ClassID', 1, 'Position', [111.0 -3.6 0]);
vehicle(scenario, 'ClassID', 1, 'Position', [140.6 -3.6 0]);
vehicle(scenario, 'ClassID', 1, 'Position', [182.6 -3.6 0]);
vehicle(scenario, 'ClassID', 1, 'Position', [211.3 -4.1 0]);

% Add pedestrian
actor(scenario, 'ClassID', 4, 'Length', 0.5, 'Width', 0.5, ...
    'Height', 1.7, 'Position', [130.3 -2.7 0], 'RCSPattern', [-8 -8;-8 -8]);

% Add parked truck
vehicle(scenario, 'ClassID', 2, 'Length', 8.2, 'Width', 2.5, ...
    'Height', 3.5, 'Position', [117.5 -3.5 0]);
end

function [sensors, numSensors, attachedVehicle] = createSensors(scenario)
% createSensors Returns all sensor objects to generate detections
% Units used in createSensors and createDrivingScenario
% Distance/Position - meters
% Speed             - meters/second
% Angles            - degrees
% RCS Pattern       - dBsm

% Assign into each sensor the physical and radar profiles for all actors
profiles = actorProfiles(scenario);
sensors{1} = radarDetectionGenerator('SensorIndex', 1, ...
    'SensorLocation', [3.7 0], 'MaxRange', 50, 'FieldOfView', [60 5], ...
    'ActorProfiles', profiles, 'HasOcclusion', true, 'HasFalseAlarms', false);
sensors{2} = visionDetectionGenerator('SensorIndex', 2, ...
    'MaxRange', 100, 'SensorLocation', [1.9 0], 'DetectorOutput', 'Objects only', ...
    'ActorProfiles', profiles);
sensors{3} = radarDetectionGenerator('SensorIndex', 3, ...
    'SensorLocation', [3.7 0], 'MaxRange', 120, 'FieldOfView', [30 5], ...
    'ActorProfiles', profiles, 'HasOcclusion', true, 'HasFalseAlarms', false);
attachedVehicle = [1;1;2];
numSensors = numel(sensors);
end

function trackInEgo = scenarioToEgo(trackInScenario)
% Performs coordinate transformation from scenario to ego coordinates
% trackInScenario has StateParameters defined to transform it from scenario
% coordinates to ego coordinates
% We assume a constant velocity model with state [x;vx;y;vy;z;vz]
egoPosInScenario = trackInScenario.StateParameters.Position;
egoVelInScenario = trackInScenario.StateParameters.Velocity;
stateInScenario = trackInScenario.State;
stateShift = [egoPosInScenario(1);egoVelInScenario(1);egoPosInScenario(2);egoVelInScenario(2);egoPosInScenario(3);egoVelInScenario(3)];
stateInEgo = stateInScenario - stateShift;
trackInEgo = objectTrack('UpdateTime',trackInScenario.UpdateTime,'State',stateInEgo,'StateCovariance',trackInScenario.StateCovariance,'StateParameters',trackInScenario.StateParameters);
end

function trackInScenario = egoToScenario(trackInEgo)
% Performs coordinate transformation from ego to scenario coordinates
% trackInEgo has StateParameters defined to transform it from ego
% coordinates to scenario coordinates
% We assume a constant velocity model with state [x;vx;y;vy;z;vz]
egoPosInScenario = trackInEgo.StateParameters.Position;
egoVelInScenario = trackInEgo.StateParameters.Velocity;
stateInScenario = trackInEgo.State;
stateShift = [egoPosInScenario(1);egoVelInScenario(1);egoPosInScenario(2);egoVelInScenario(2);egoPosInScenario(3);egoVelInScenario(3)];
stateInEgo = stateInScenario + stateShift;
trackInScenario = objectTrack('UpdateTime',trackInEgo.UpdateTime,'State',stateInEgo,'StateCovariance',trackInEgo.StateCovariance,'StateParameters',trackInEgo.StateParameters);
end

function [tracks,wasTrackerUpdated] = detectAndTrack(agent,time,posSelector)
% Create detections from the vehicle
poses = targetPoses(agent.Actor);
[detections,isValid] = vehicleDetections(agent.Actor.Position,agent.Sensors,poses,time,agent.DetPlotter);

% Update tracks for the vehicle
if isValid
    agent.Tracker.StateParameters = struct(...
        'Frame','Rectangular', ...
        'Position', agent.Actor.Position, ...
        'Velocity', agent.Actor.Velocity);
    tracks = agent.Tracker(detections,time);
    tracksInScenario = tracks;
    for i = 1:numel(tracks)
        tracksInScenario(i) = egoToScenario(tracks(i));
    end
    pos = getTrackPositions(tracksInScenario,posSelector);
    plotTrack(agent.TrkPlotter,pos)
    wasTrackerUpdated = true;
else
    tracks = objectTrack.empty(0,1);
    wasTrackerUpdated = false;
end
end

function [objectDetections,isValid] = vehicleDetections(position, sensors, poses, time, plotter)
% Provides the detections for each vehicle. 

numSensors = numel(sensors);
objectDetections = {};
isValidTime      = false(1, numSensors);

% Generate detections for each sensor
for sensorIndex = 1:numSensors
    sensor = sensors{sensorIndex};
    [objectDets, ~, isValidTime(sensorIndex)] = sensor(poses, time);
    objectDets = cellfun(@(d) setAtt(d), objectDets, 'UniformOutput', false);
    
    if isa(sensors{sensorIndex},'radarDetectionGenerator')
        objectDets = helperClusterDetections(objectDets, 5);
    end
    numObjects = numel(objectDets);
    objectDetections = [objectDetections; objectDets(1:numObjects)]; %#ok<AGROW>
end
isValid = any(isValidTime);

% Plot detections
if numel(objectDetections)>0
    detPos = cellfun(@(d)d.Measurement(1:2), objectDetections, 'UniformOutput', false);
    detPos = cell2mat(detPos')' + position(1:2);
    plotDetection(plotter, detPos);
end
end

function d = setAtt(d)
% Set the attributes to be struct
d.ObjectAttributes = struct;
end

function detectionClusters = helperClusterDetections(detections, vehicleSize)
% helperClusterDetections  Helper to cluster detections in the example
N = numel(detections);
distances = zeros(N);
for i = 1:N
    for j = i+1:N
        if detections{i}.SensorIndex == detections{j}.SensorIndex
            distances(i,j) = norm(detections{i}.Measurement(1:2) - detections{j}.Measurement(1:2));
        else
            distances(i,j) = inf;
        end
    end
end
leftToCheck = 1:N;
i = 0;
detectionClusters = cell(N,1);
while ~isempty(leftToCheck)    
    % Remove the detections that are in the same cluster as the one under
    % consideration
    underConsideration = leftToCheck(1);
    clusterInds = (distances(underConsideration, leftToCheck) < vehicleSize);
    detInds = leftToCheck(clusterInds);
    clusterDets = [detections{detInds}];
    clusterMeas = [clusterDets.Measurement];
    meas = mean(clusterMeas, 2);
    i = i + 1;
    detectionClusters{i} = detections{detInds(1)};
    detectionClusters{i}.Measurement = meas;
    leftToCheck(clusterInds) = [];    
end
detectionClusters(i+1:end) = [];

% Since the detections are now for clusters, modify the noise to represent
% that they are of the whole car
for i = 1:numel(detectionClusters)
    measNoise = eye(6);
    measNoise(1:2,1:2) = vehicleSize^2 * eye(2);
    measNoise(4:5,4:5) = eye(2) * vehicleSize^2;
    detectionClusters{i}.MeasurementNoise = measNoise;
end
end