Simcenter Prescan How to use Data model API in Prescan
2022-08-23T07:54:17.000-0400
Prescan
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Simcenter Prescan Marketing
Summary
Data model API is a function that allows user to create the content of experiment via a MATLAB
Details
Step1: Create an empty experiment using GUI, build the experiment and save it.
Step2: Open Matlab via Prescan process manager and navigate to your experiment created from GUI
Step 3: In MATLAB create a new script and save it in the experiment folder
Step 4: Inside the script we can start building the model. User can access DMAPI and module using command doc prescan.api
Example script for reference
exp = prescan.api.experiment.Experiment();
%% Create a road
road_1 = prescan.api.roads.createRoad(exp);
%% Add sections to the road
sectionLength = 250; % [m]
road_1.addStraightSection(sectionLength);
sectionLength = 50;
startCurvature = 0.025;
endCurvature = 0.1;
road_1.addSpiralSection(sectionLength, startCurvature, endCurvature);
%% Once the sections have been added, add lanes to the road
lane1 = road_1.addLeftLane(3.2);
marker = lane1.getLaneMarker('Outer');
marker.setType('Broken');
lane2 = road_1.addLeftLane(3.2);
marker = lane2.getLaneMarker('Outer');
marker.setType('Solid');
lane3 = road_1.addRightLane(3.2);
marker = lane3.getLaneMarker('Outer');
marker.setType('Broken');
lane4 = road_1.addRightLane(3.2);
marker = lane4.getLaneMarker('Outer');
marker.setType('Solid');
centerOfLane1 = lane3.poseAtDistance(0.0).position.y;
centerOfLane2 = lane4.poseAtDistance(0.0).position.y;
edgeOfRoadLeft = lane2.poseAtDistance(0.0, 'Outer', 10.0).position.y;
edgeOfRoadRight = lane4.poseAtDistance(0.0, 'Outer', 10.0).position.y;
%% Add objects to the road
vehicleAudi = exp.createObject(exp.objectTypes.Audi_A8_Sedan);
vehicleAudi.name = 'Ego';
% Trees
for i = 0:35
r = -3 + (3+3).*rand(3,1); % Make it random
xtree = 6 * i + r(1);
ytree1 = edgeOfRoadLeft + r(2);
ytree2 = edgeOfRoadRight + r(3);
tree = exp.createObject(exp.objectTypes.Dogwood20y);
tree.pose.position.x = xtree;
tree.pose.position.y = ytree1;
tree = exp.createObject(exp.objectTypes.Dogwood20y);
tree.pose.position.x = xtree;
tree.pose.position.y = ytree2;
end
%% Position the vehicles correctly
vehicleAudi.pose.position.y = centerOfLane2;
%% Add a trajectory to the ego vehicle
z = [0, 0];
y = [vehicleAudi.pose.position.y, vehicleAudi.pose.position.y];
x = [vehicleAudi.pose.position.x, 200];
pathAudi = prescan.api.trajectory.createFittedPath(exp, x, y, z);
speedProf = prescan.api.trajectory.createSpeedProfileOfConstantSpeed(exp, 5);
trajectoryAudi = prescan.api.trajectory.createTrajectory(vehicleAudi, pathAudi, speedProf);
%% Add a camera to the ego vehicle
cameraAudi = prescan.api.camera.createCameraSensor(vehicleAudi);
%% Save the experiment to a file
exp.saveToFile('DMAPI.pb');
%% Generate the CS
prescan.api.simulink.generate();
%% Run the experiment
prescan.api.simulink.run(exp, 'Regenerate', 'off', 'StopTime', '5')
Step5: Once the required changes are done to the experiment, save the file and run the script.
Step 6: Load the PB file in the Prescan static viewer and see the experiment built using API.
Step2: Open Matlab via Prescan process manager and navigate to your experiment created from GUI
Step 3: In MATLAB create a new script and save it in the experiment folder
Step 4: Inside the script we can start building the model. User can access DMAPI and module using command doc prescan.api
Example script for reference
exp = prescan.api.experiment.Experiment();
%% Create a road
road_1 = prescan.api.roads.createRoad(exp);
%% Add sections to the road
sectionLength = 250; % [m]
road_1.addStraightSection(sectionLength);
sectionLength = 50;
startCurvature = 0.025;
endCurvature = 0.1;
road_1.addSpiralSection(sectionLength, startCurvature, endCurvature);
%% Once the sections have been added, add lanes to the road
lane1 = road_1.addLeftLane(3.2);
marker = lane1.getLaneMarker('Outer');
marker.setType('Broken');
lane2 = road_1.addLeftLane(3.2);
marker = lane2.getLaneMarker('Outer');
marker.setType('Solid');
lane3 = road_1.addRightLane(3.2);
marker = lane3.getLaneMarker('Outer');
marker.setType('Broken');
lane4 = road_1.addRightLane(3.2);
marker = lane4.getLaneMarker('Outer');
marker.setType('Solid');
centerOfLane1 = lane3.poseAtDistance(0.0).position.y;
centerOfLane2 = lane4.poseAtDistance(0.0).position.y;
edgeOfRoadLeft = lane2.poseAtDistance(0.0, 'Outer', 10.0).position.y;
edgeOfRoadRight = lane4.poseAtDistance(0.0, 'Outer', 10.0).position.y;
%% Add objects to the road
vehicleAudi = exp.createObject(exp.objectTypes.Audi_A8_Sedan);
vehicleAudi.name = 'Ego';
% Trees
for i = 0:35
r = -3 + (3+3).*rand(3,1); % Make it random
xtree = 6 * i + r(1);
ytree1 = edgeOfRoadLeft + r(2);
ytree2 = edgeOfRoadRight + r(3);
tree = exp.createObject(exp.objectTypes.Dogwood20y);
tree.pose.position.x = xtree;
tree.pose.position.y = ytree1;
tree = exp.createObject(exp.objectTypes.Dogwood20y);
tree.pose.position.x = xtree;
tree.pose.position.y = ytree2;
end
%% Position the vehicles correctly
vehicleAudi.pose.position.y = centerOfLane2;
%% Add a trajectory to the ego vehicle
z = [0, 0];
y = [vehicleAudi.pose.position.y, vehicleAudi.pose.position.y];
x = [vehicleAudi.pose.position.x, 200];
pathAudi = prescan.api.trajectory.createFittedPath(exp, x, y, z);
speedProf = prescan.api.trajectory.createSpeedProfileOfConstantSpeed(exp, 5);
trajectoryAudi = prescan.api.trajectory.createTrajectory(vehicleAudi, pathAudi, speedProf);
%% Add a camera to the ego vehicle
cameraAudi = prescan.api.camera.createCameraSensor(vehicleAudi);
%% Save the experiment to a file
exp.saveToFile('DMAPI.pb');
%% Generate the CS
prescan.api.simulink.generate();
%% Run the experiment
prescan.api.simulink.run(exp, 'Regenerate', 'off', 'StopTime', '5')
Step5: Once the required changes are done to the experiment, save the file and run the script.
Step 6: Load the PB file in the Prescan static viewer and see the experiment built using API.