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addSensors

Add sensors to vehicle actors in RoadRunner scenario

Since R2023a

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Syntax

addSensors(sensorSim,sensors,actorID)
addSensors(sensorSim,sensorBlkPath,actorID)

Description

example

addSensors(sensorSim,sensors,actorID) adds the specified sensors in sensors to the vehicle actor with ID actorID in the RoadRunner scenario specified by scenarioSim. Use this syntax when you define sensor models in MATLAB®.

example

addSensors(sensorSim,sensorBlkPath,actorID) adds the sensors with the specified block paths sensorBlkPath to the vehicle actor with ID actorID in the roadrunner scenario specified by scenarioSim. Use this syntax when you define sensor models in Simulink®.

Examples

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This example uses:

Open Live Script

Define sensor models in MATLAB®, and add them to vehicle actors in a RoadRunner Scenario. Then, obtain ground truth measurements from RoadRunner Scenario, process them into detections for visualization.

Set Up RoadRunner Scenario — MATLAB Interface

Configure your RoadRunner installation and project folder properties. Open the RoadRunner app.

rrInstallationPath = "C:\Program Files\RoadRunner R2023b\bin\win64";
rrProjectPath = "D:\RR\TestProjects";

s = settings;
s.roadrunner.application.InstallationFolder.PersonalValue = rrInstallationPath;
rrApp = roadrunner(rrProjectPath);

To open the scenario this example uses, you must add the TrajectoryCutIn-longRun.rrscenario file from the example folder to your RoadRunner project folder. Then, open the scenario.

openScenario(rrApp,"TrajectoryCutIn-longRun")

Create a ScenarioSimulation object to connect MATLAB to the RoadRunner Scenario simulation and set the step size. 

scenarioSim = createSimulation(rrApp);
stepSize = 0.1;
set(scenarioSim,"StepSize",stepSize);

Create a SensorSimulation object to control the sensor configuration for the RoadRunner Scenario simulation.

sensorSim = get(scenarioSim,"SensorSimulation");

Configure Sensors and Add to RoadRunner Scenario

Configure sensor models for vision, radar and lidar sensors to add to the ego vehicle using visionDetectionGenerator, drivingRadarDataGenerator and lidarPointCloudGenerator objects. Specify unique IDs for each sensor.

visionSensor = visionDetectionGenerator(SensorIndex=1, ...
            SensorLocation=[2.4 0], MaxRange=50, ...
            DetectorOutput="Lanes and objects", ...
            UpdateInterval=stepSize);

radarSensor = drivingRadarDataGenerator(SensorIndex=2,...
    MountingLocation=[1.8 0 0.2], FieldOfView=[80 5],...
    AzimuthResolution=1,UpdateRate=1/stepSize);

lidarSensor = lidarPointCloudGenerator(SensorIndex=3,UpdateInterval=stepSize);

Add the sensor to the ego vehicle actor in the RoadRunner scenario. Specify the Actor ID property for the vehicle.

egoVehicleID = 1;
addSensors(sensorSim,{visionSensor,radarSensor,lidarSensor},egoVehicleID);

Set up bird's-eye-plot for visualization.

[visionDetPlotter,radarDetPlotter,pcPlotter,lbGTPlotter,lbDetPlotter,bepAxes] = helperSetupBEP(visionSensor,radarSensor);

Simulate RoadRunner Scenario and Visualize Sensor Data

Use the ScenarioSimulation object to step through the RoadRunner scenario. Retrieve target poses in the sensor range using the targetPoses function, and process them into detections using the sensor model. Visualize detections and ground truth lane boundaries using birdsEyePlot. 

simTime = 0.0;
set(scenarioSim,"SimulationCommand","Step");
pause(0.1)
legend(bepAxes,"show")

while ~isequal(get(scenarioSim,"SimulationStatus"),"Stopped")

    % Get ground truth target poses and lane boundaries from the sensor
    tgtPoses = targetPoses(sensorSim,1);
    gTruthLbs = laneBoundaries(sensorSim,1,OutputOption="EgoAdjacentLanes",inHostCoordinate=true);
    
    if ~isempty(gTruthLbs)
        
        % Get detections from vision and radar sensors
        [visionDets,numVisionDets,visionDetsValid,lbDets,numLbDets,lbDetsValid] = visionSensor(tgtPoses,gTruthLbs,simTime);
        [radarDets,numRadarDets,radarDetsValid] = radarSensor(tgtPoses,simTime);

        % Get point point cliud from lidar sensor
        [ptCloud,ptCloudValid] = lidarSensor();

        % Plot ground-truth and detected lane boundaries
        helperPlotLaneBoundaries(lbGTPlotter,gTruthLbs)
       
        
        % Plot vision and radar detections
        if visionDetsValid
            detPos = cellfun(@(d)d.Measurement(1:2),visionDets,UniformOutput=false);
            detPos = vertcat(zeros(0,2),cell2mat(detPos')');
            plotDetection(visionDetPlotter,detPos)
        end

        if lbDetsValid
            plotLaneBoundary(lbDetPlotter,vertcat(lbDets.LaneBoundaries))
        end

        if radarDetsValid
            detPos = cellfun(@(d)d.Measurement(1:2),radarDets,UniformOutput=false);
            detPos = vertcat(zeros(0,2),cell2mat(detPos')');
            plotDetection(radarDetPlotter,detPos)
        end

        % Plot lidar point cloud
        if ptCloudValid
            plotPointCloud(pcPlotter,ptCloud);
        end
    end     

    if ~isequal(get(scenarioSim,"SimulationStatus"),"Stopped")
        set(scenarioSim,"SimulationCommand","Step");
    end

    simTime = simTime + stepSize;
    pause(0.1)
end

Helper Functions

helperSetupBEP function creates a bird's-eye-plot and configures all the plotters for visualization.

helperPlotLaneBoundaries function plots the lane boundaries on the birds'eye-plot.

This example uses:

Open Live Script

Define sensor models in Simulink®, and add them to vehicle actors in a RoadRunner Scenario. Then, obtain ground truth measurements from RoadRunner Scenario, process them into detections in Simulink for visualization.

Set Up RoadRunner Scenario — Simulink Interface

Configure your RoadRunner installation and project folder properties. Open the RoadRunner app.

rrInstallationPath = "C:\Program Files\RoadRunner R2023b\bin\win64";
rrProjectPath = "D:\RR\TestProjects";

s = settings;
s.roadrunner.application.InstallationFolder.PersonalValue = rrInstallationPath;
rrApp = roadrunner(rrProjectPath);

To open the scenario this example uses, you must add these files from the example folder to your RoadRunner project folder:

  1. straightCurvedFourLaneRoad.rrscene — RoadRunner scene file that describes the four lane highway.

  2. SensorDerectionSimulation.rrscenario — RoadRunner scenario file that describes actors and their trajectories on the four lane highway scene.

  3. SensorIntegration.rrbehavior.rrmeta — Behavior file that associates the sensor detection processing and visualization behavior using Simulink, to the ego vehicle in the RoadRunner scenario. 

copyfile("straightCurvedFourLaneRoad.rrscene",fullfile(rrProjectPath,"Scenes/"));
copyfile("SensorDetectionSimulation.rrscenario",fullfile(rrProjectPath,"Scenarios/"))
copyfile("SensorIntegration.rrbehavior.rrmeta",fullfile(rrProjectPath,"Assets","Behaviors/"))

Open the scenario and create a ScenarioSimulation object to connect Simulink with the RoadRunner Scenario.

openScenario(rrApp,"SensorDetectionSimulation")
rrSim = createSimulation(rrApp);

Specify the step size for the RoadRunner scenario simulation. The Simulink model also uses the same step size. Units are in seconds.

simStepSize = 0.1;
set(rrSim,"StepSize",simStepSize);

Inspect Simulink Model and Simulate Scenario

The Simulink model rrScenarioSimWithSensors configures sensor models for the vision, radar and lidar sensors using the Vision Detection Generator, Driving Radar Data Generator and Lidar Point Cloud Generator blocks respectively. The model reads ground truth data from the scenario and processes that into detections and visualizes the detections. It also reads the path defined for the ego vehicle from the RoadRunner Scenario, implements the path follower logic and controls the ego vehicle motion along the path.

Load the bus definitions and open the model. Note that the lidar sensor model block does not require any ground-truth target pose inputs.

load("busDefinitionsForRRSim.mat")
modelName = 'rrScenarioSimWithSensors';
open_system(modelName)

Create a SensorSimulation object. Specify the block paths for the radar, vision and lidar sensors in the model.

sensorSim = get(rrSim,"SensorSimulation");
visionSensorBlkPath = [modelName,'/Vision Detection Generator'];
radarSensorBlkPath = [modelName,'/Driving Radar Data Generator'];
lidarSensorBlkPath = [modelName,'/Lidar Point Cloud Generator'];

Add the three sensors to the ego vehicle using the addSensors function. 

egoVehicleId = 1;
addSensors(sensorSim,{visionSensorBlkPath,radarSensorBlkPath,lidarSensorBlkPath},egoVehicleId);

To visualize the scenario and sensor detections, use the Bird'sEye Scope app. On the Simulink toolstrip, under Review Results, click Bird's-Eye Scope. Then in the scope, click Find Signals to establish connection with the RoadRunner scenario and populate the signals to visualize.

Start the scenario Simulation. The model visualizes the 3D point cloud using the Point Cloud Viewer block during the simulation. In Bird's-Eye Scope, you can visualize the 2D point cloud, object detections and lane detections alongside ground truth values.

set(rrSim,"SimulationCommand","Start")

Input Arguments

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Sensor simulation object, specified as a SensorSimulation object. The sensor simulation object must correspond to the desired scenario created in RoadRunner Scenario.

Sensors to add to the RoadRunner Scenario simulation, specified as one of these options:

Each detection generator must have a unique sensor ID, specified by its SensorIndex property.

Block paths of sensors to add to the RoadRunner Scenario simulation, specified as a cell array of character vectors. Each block path must point to one of these detection generator blocks:

Each detection generator must have a unique sensor ID, specified by its Unique sensor identifier block parameter.

Actor ID of the vehicle in the RoadRunner scenario, specified as a positive integer. The function adds sensors to the vehicle in the scenario with this actor ID.

Version History

Introduced in R2023a

See Also

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