Example Archives - RIDE https://ride.ict.usc.edu/category/example/ Rapid Integration & Development Environment Tue, 28 Apr 2026 16:28:12 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://ride.ict.usc.edu/wp-content/uploads/2020/05/cropped-RIDE_Logo_Icon_250x-32x32.png Example Archives - RIDE https://ride.ict.usc.edu/category/example/ 32 32 Large Language Model Comparison https://ride.ict.usc.edu/support/documentation/examples/large-language-model-comparison/?utm_source=rss&utm_medium=rss&utm_campaign=large-language-model-comparison Fri, 08 Dec 2023 00:05:07 +0000 https://ride.ict.usc.edu/?page_id=6818 Demonstrates cloud-based large language models (LLMs) generative AI, comparing OpenAI ChatGPT against Anthropic Claude.

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Example

Large Language Model Comparison

Overview

Demonstrates cloud-based large language models (LLMs) generative AI, comparing OpenAI ChatGPT against Anthropic Claude.

Page Contents

Example LLM RIDE Chat
ExampleLLM

Purpose

Shows how to leverage large language model generative AI services through REST calls, using RIDE’s common web services system.

Leverages RIDE’s generic NLP C# interface, primarily focused on question-answering interactions, with the principle generally working for user input and agent output. Optionally includes sentiment analysis and entity analysis.

Current Providers

  • OpenAI ChatGPT-4
  • Anthropic – Claude

How to Use

IMPORTANT

If you wish to use the ExampleLLM scene and related web services, you must update your local RIDE config file (ride.json). Please back-up any local changes to the file, including your terrain key. Next, restore your Config to default using the corresponding option in the Config debug menu at run-time, then reapply any customization and terrain key. Refer to the Config File page within Support for more information. 

Provider Comparison UI

  1. Input text in the bottom field and hit enter or click the Ask button. Any input will be sent to both providers simultaneously.
  2. Each service has its own pane which populates with the following information after entering a query:
    1. Response Time
    2. Max Token Count
    3. Returned answer from the provider

Scene Location & Name

Assets/Ride/Examples/NLP/ExampleLLM/ExampleLLM.unity

Setup Requirements 

The ExampleLLM scene utilizes a customizable UI and interchangeable service providers through canvas, scripts and prefabs. Explore the objects in the Hierarchy view for the scene inside the Unity editor and source for Ride.NLP via the API documentation.

The main script is ExampleLLM.cs in the same folder.

Any NLP option implements the INLPQnASystem C# Interface, which itself is based on INLPSystem. These systems provide the main building blocks to interface with an NLP service:

				
					
OpenAIGPT3System m_openAIGPT3;
var openAIGPT3Component = new GameObject("OpenAIGPT3System").AddComponent<OpenAIGPT3System>();
m_openAIGPT3 = openAIGPT3Component;

The specific endpoint and authentication information is defined through RIDE’s configuration system:

				
					
m_configSystems = Globals.api.systemAccessSystem.GetSystem<RideConfigSystem>();
openAIGPT3Component.m_uri = m_configSystems.config.openAI.endpoint;
openAIGPT3Component.m_authorizationKey = m_configSystems.config.openAI.endpointKey;

The NLP system encapsulates the user question into an NLPQnAQuestion or NLPRequest object. This user input is sent through the RIDE common web services system. The results are encapsulated into an NLPQnAAnswer  or NLPResponse object. A provided callback function allows a client (in this case this ExampleNLP) what specifically to do with the result. The NLP system itself handles much of the work, so that the client only requires one line to send the user question: 

				
					
m_openAIGPT3.AskQuestion(userInput, OnCompleteAnswer);

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]]> Image Generation Comparison https://ride.ict.usc.edu/support/documentation/examples/image-generation-comparison/?utm_source=rss&utm_medium=rss&utm_campaign=image-generation-comparison Wed, 06 Dec 2023 00:02:45 +0000 https://ride.ict.usc.edu/?page_id=6771 Demonstrates cloud-based text-to-image generative AI, comparing OpenAI DALL-E against Stability AI Stable Diffusion.

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Example

Image Generation Comparison

Overview

Demonstrates cloud-based text-to-image generative AI, comparing OpenAI DALL-E against Stability AI Stable Diffusion.

Page Contents

Example Image Generation Capture
ExampleImageGeneration

Purpose

Shows how to leverage commodity text-to-image generative AI services through REST calls, using RIDE’s common web services system.

Leverages RIDE’s Generative AI and Image Generation Interfaces which implement OpenAI DALL-E and Stability AI Stable Diffusion.

Current Providers

  • OpenAI DALL-E
  • Stability AI Stable Diffusion

How to Use

IMPORTANT

If you wish to use the ExampleImageGeneration scene and related web services, you must update your local RIDE config file (ride.json). Please back-up any local changes to the file, including your terrain key. Next, restore your Config to default using the corresponding option in the Config debug menu at run-time, then reapply any customization and terrain key. Refer to the Config File page within Support for more information. 

Provider Comparison UI

  1. Input text in the bottom field and hit enter or click the Send Prompt. Any input will be sent to both providers simultaneously.
  2. Each service has its own pane which populates with the following information after entering a query:
    1. Time to generate and receive
    2. Returned image from the provider

Scene Location & Name

Assets/Ride/Examples/ExampleImageGeneration.unity

Setup Requirements 

The ExampleImageGeneration scene utilizes a customizable UI and interchangeable service providers through canvas, scripts and prefabs. Explore the objects in the Hierarchy view for the scene inside the Unity editor and source via the API documentation.

The main script is ExampleImageGeneration.cs in the same folder.

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]]> Asset Lineup https://ride.ict.usc.edu/support/documentation/examples/asset-lineup/?utm_source=rss&utm_medium=rss&utm_campaign=asset-lineup Thu, 06 Jul 2023 19:04:47 +0000 https://ride.ict.usc.edu/?page_id=5810 Collects available entity prefabs for inspection, modification and testing.

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Example

Asset Lineup

Overview

Collects available entity prefabs for inspection, modification and testing.

Page Contents

RIDE Asset Lineup Soldiers
RideAssetLineUp

Purpose

Collects available entity prefabs for inspection, modification and testing.

How to Use

This scene includes prefabs strictly limited to DoD distribution of Ride, Ride_Art, and if useful, Ride_Dependencies directories. Consult the 3rdPartyLicenses document in the DoD distribution for specifics about the Unity Asset Store and other 3rd party vendor asset fees and restrictions.

Prefab variants of soldiers and other units non-exhaustive.

Entities are grouped by type in-scene:

  • Characters
  • Weapons
  • Vehicles
  • Props 

Note, LODs and unit properties unmodified except where to prevent combat engagement.

Controls

WASD keys – Move camera

Q, E keys – Rotate camera

RMB – Hold for mouse look

Scene Location & Name

Assets/Ride/Examples/RideAssetLineUp/RideAssetLineUp.unity

Setup Requirements 

The initial version of the scene utilizes the RideBaseMinimal script, DirectionalLightRig, and a simple plane object.

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]]> Audio-Visual Sensing https://ride.ict.usc.edu/support/documentation/examples/audio-visual-sensing/?utm_source=rss&utm_medium=rss&utm_campaign=audio-visual-sensing Tue, 10 Jan 2023 01:26:23 +0000 https://ride.ict.usc.edu/?page_id=5490 Shows how to leverage commodity audio-visual sensing AI services through REST calls, using RIDE's common web services system.

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Example

Audio-Visual Sensing

Overview

Shows how to leverage commodity audio-visual sensing AI services through REST calls, using RIDE’s common web services system.

Page Contents

Example Face Image 2
ExampleFace

Purpose

Shows how to leverage commodity audio-visual sensing AI services through REST calls, using RIDE’s common web services system.

Leverages RIDE’s generic Sensing C# interface, where a (webcam) image is sent to a server and resulting analysis data is received. 

Initially supported service is Microsoft Azure Face, which provides:

  • Emotion values (e.g., anger, happiness, neutral)
  • Head values (e.g., pitch, roll, occlusions)
  • Characteristics (e.g., gender, age)

Note, Characteristics and Emotion values recently deprecated.

How to Use

Make sure you have a webcam set up and run the scene. Your webcam view should be seen at the top right. Click Cycle if you have multiple webcams.

The Single button sends a single webcam image to the server. Results are shown underneath.

The Start button starts the process of sending an image every second. Stop stops this process.

Scene Location & Name

\Assets\Ride\Examples\Services\Sensing\ExampleFace.unity

Setup Requirements 

The main script is ExampleFace.cs in the same folder.

Any Sensing option implements the ISensingSystem C# Interface. These systems provide the main building blocks to interface with a sensing service:

				
					
ISensingSystem m_azureFace;

The specific endpoint and authentication information is defined through RIDE’s configuration system:

				
					
RideConfigSystem m_configSystem = Globals.api.systemAccessSystem.GetSystem<RideConfigSystem>();
   string requestParameters = "returnFaceId=true&returnFaceLandmarks=false&returnFaceAttributes=age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise";
   m_uriAzureFace = m_configSystem.config.azureFace.endpoint + "/face/v1.0/detect?" + requestParameters;
   var azureFaceComponent = new GameObject("AzureFaceSystem").AddComponent<AzureFaceSystem>();
   azureFaceComponent.m_uri = m_uriAzureFace;
   azureFaceComponent.m_endpointKey = m_configSystem.config.azureFace.endpointKey;
   m_azureFace = azureFaceComponent;

The Sensing system encapsulates the input into an SensingRequest object. This user input is sent through the RIDE common web services system. The results are encapsulated into an SensingResponse object. A provided callback function allows a client (in this case this ExampleFace) what specifically to do with the result. The Sensing system itself handles much of the work, so that the client only requires one line to send the user input:

				
					
Globals.api.systemAccessSystem.GetSystem<ISensingEmotionSystem>(m_azureFace.id).AnalyzeEmotions(m_imageData, OnCompleteEmotion);

Referenced by ExampleGroups script for group definitions.

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]]> Web Services https://ride.ict.usc.edu/support/documentation/examples/web-services/?utm_source=rss&utm_medium=rss&utm_campaign=web-services Tue, 10 Jan 2023 01:10:55 +0000 https://ride.ict.usc.edu/?page_id=5477 Shows how to send GET, PUT, POST, and web requests through RIDE's common web services system.

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Example

Web Services

Overview

Shows how to send GET, PUT, POST, and web requests through RIDE’s common web services system.

Page Contents

Example Web Services Image 03
ExampleWebServices

Purpose

Shows how to send GET, PUT, POST, and web requests through RIDE’s common web services system.

How to Use

Run the scene, enter any input on the left-hand side, click the Send button, and see any output on the right-hand side.

Clicking the Examples button cycles through various pre-filled examples. Clicking Get, Put, or Post resets the input fields.

Scene Location & Name

\Assets\Ride\Examples\Services\WebServices\ExampleWebServices.unity

Setup Requirements 

The main script is ExampleWebServices.cs in the same folder.

SetRequestType() contains SetUIContent() with parameters to fill in the fields.

SendRequest() contains the main code for setting up and sending the request. It uses one of various functions in RideIO.cs (e.g., Post(), Put(), etc.), part of RideUtils. Most of these have overrides.

Note that RideUtils also provides a connection check: RideUtils.IsInternetConnectionAvailable()

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]]> OAuth https://ride.ict.usc.edu/support/documentation/examples/oauth/?utm_source=rss&utm_medium=rss&utm_campaign=oauth Wed, 09 Nov 2022 23:56:00 +0000 https://ride.ict.usc.edu/?page_id=5417 This example demonstrates how to use Google and Facebook OAuth authentication systems within RIDE.

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Example

OAuth

Overview

This example demonstrates how to use Google and Facebook OAuth authentication systems within RIDE.

Page Contents

Example OAuth Page
ExampleOAuth

Purpose

This example demonstrates how to use Google and Facebook OAuth authentication systems within RIDE.

How to Use

Facebook

  1. Choose Facebook.

  2. Fill in the things as under from Setup Requirements section below:
    1. Firebase API Key: Step-2.
    2. Facebook App ID: Step-9(j).
    3. Facebook App Secret: Step-9(j).
    4. Redirect / SaveAuthToken URL: Step-5(n)-i.
    5. GetAuthToken URL: Step-5(n)-ii.

  3. Click on Login Facebook button verifying everything in Step-2 and it will redirect you to the web page to ask Allow login to Facebook. Enter your Facebook credentials and click Continue.

  4. A webpage will pop up telling to return to the app and you will see your name, email and profile picture pop up in the Unity App indicating that the login was successful.

  5. Click the Clear button to clear the account profile pic, email, and name.
  6. Click Back to return to the login selection menu.

Google

  1. Choose Google.

  2. Fill in the things as under from Setup Requirements section below:
    1. Firebase API Key: Step-2.
    2. Google Client ID: Step-7(a)-i.
    3. Google Client Secret: Step-7(a)-i.
    4. Redirect / SaveAuthToken URL: Step-5(n)-i.
    5. GetAuthToken URL: Step-5(n)-ii.

  3. Click on Login Google button verifying everything in Step-2 and it will redirect you to the web page to ask Allow login to Google. Enter your Google credentials or choose from a list of accounts.

  4. A webpage will pop up telling you to return to the app and you will see your email pop up in the Unity App indicating that the login was successful.

  5. Click the Clear button to clear the account profile pic, email, and name.
  6. Click Back to return to the login selection menu.

Scene Location & Name

Assets/Ride/Examples/OAuth/Scenes/ExampleOAuth.unity 

Scene Setup

The example Unity scene directory contains the backend scripts related to the authentication services that are presented in a custom UI within the scene: ExampleOauth.cs, FirebaseAuthHandler.cs, GoogleAuthenticator.cs, FacebookAuthenticator.cs, FacebookIdTokenResponse.cs and GoogleIdTokenResponse.cs.

Server Setup

Enable custom authentication cloud services for your own project using the following process:

  1. Create a Google Cloud project which will act as the master project in the Google Cloud Console https://console.cloud.google.com/. Make sure you have the developer or admin privileges.
  2. Go to Firebase console https://console.firebase.google.com/ and you will see the same project already there. Select it. Keep a note of the Web API Key under Project Overview→Project Settings.
    1. Note, the key may not appear immediately. Navigate to Authentication and step through the various tabs (Users, etc.) then return to the Project Settings whereupon it should now populate. 
  3. Next, create a new database for logging all related services data. 
    1. In the Firebase console, navigate to Build→Realtime Database.
    2. Click Create Database button.
    3. Click Next in the prompt.
    4. Choose Start in Locked Mode.
    5. Click Enable.
    6. Empty database with corresponding URL should now appear in the main pane.
  4. In the Build→Functions section we need to create and invoke two cloud functions. 
    1. Firebase Cloud Function is a backend for Firebase that lets user deploy JavaScript/TypeScript code onto Firebase and then run it when required. It is used here as a web page to retrieve the authentication code required to get a user sign in to Google, Facebook or any other providers.
  5. Invoking Cloud Function on Firebase Server:
    1. Go to https://nodejs.org/en/ and install nodejs to get the npm package installed in the system. To verify if installation was successful, type npm in command prompt and see you get no errors.

    2. Create a new folder on the desktop, naming it as desired. Open a new command prompt and change the path to that folder.
    3. Install Firebase tools using command, npm install -g firebase-tools.
      1. Once complete, recommend running ‘npm audit fix’ if vulnerabilities are reported.
    4. Login to your Firebase account using command, firebase login. It will open a browser window where you can login to your Firebase console.
      1. Answer Y or n regarding usage and error reporting information collection.
      2. Choose Allow for Firebase CLI access to your Google Account.
        1. A Firebase CLI login successful prompt will appear. Close and return to the command prompt window.
    5. Initiate Firebase instance using command, firebase init.
    6. Type Y to proceed.
    7. Choose Functions: Configure a Cloud Functions directory and its files.
    8. Choose Use existing [Firebase] project and choose it from the next option that pops up.
    9. Choose JavaScript as the language.
    10. Type N for Do you want to use ESLint to catch probable bugs and enforce style?.
    11. Type Y for Do you want to install dependencies with npm now?
      1. Once complete, you now have your firebase initialized!
    12. Copy file Assets\StreamingAssets\oauth\FirebaseCloudFunctions\index.js from the RideUnity project and replace the existing <current folder path>\functions\index.js to set the custom cloud function that will be deployed.
    13. Run command firebase deploy from the same command prompt to deploy it into the Firebase server.
    14. If everything was followed then you should see two functions named saveAuthToken and getAuthToken in the Functions section of the Firebase console with their respective associated endpoints as triggers.
      1. saveAuthToken endpoint: https://<your-region>-<project-id>.cloudfunctions.net/saveAuthToken
      2. getAuthToken endpoint: https://<your-region>-<project-id>.cloudfunctions.net/getAuthToken

  6. Google Cloud OAuth Consent Screen:
    1. In the Google Cloud console, go to API & Services→OAuth consent screen.
      1. Edit app registration, User Type, choose External, and click on CREATE.
      2. App information section fields:
        1. App name, enter custom name.
        2. User support email, enter desired email.
        3. App logo, add an image (optional).
      3. Develop contact information fields.
        1. Email address, use same email as above, add others as desired.
      4. Click SAVE AND CONTINUE.
      5. Scopes, leave as-is, click SAVE AND CONTINUE.
      6. Optional info, leave as-is, click SAVE AND CONTINUE.
      7. Summary, review and click BACK TO DASHBOARD.
  7. Google Cloud Credentials:
    1. In the Google Cloud console, click API & Services→Credentials
      1. OAuth2.0 Client IDs section, click entry name, “Web client (auto created by Google Service)”.
        1. Authorized JavaScript origins entries, ensure “URIs 3” populated with “https://<project-id>.firebaseapp.com” address.
        2. Authorized redirect URIs section, click on ADD URI, and add the SaveAuthToken endpoint generated in step-5(n)-i.
        3. Click on SAVE.
        4. Important, keep a note of the Client ID and Client secret entries listed in the upper-right.
          1. Note, if Client secret fails to appear, click RESET SECRET, then in the prompt, click RESET to generate it. 
  8. Firebase Google Login:
    1. Enable Google sign-in method in Firebase console:
      1. Go to the Firebase console under Build, Authentication→Sign-in method→Add new provider button, choose Google to add it into the list of providers.

    2. In the Configure provider pane, click the toggle to Enable, then click Save.
      1. Under Google→Web SDK configuration set the Web client ID and Web client secret from Step-5d. (Note, if not already auto-filled.)

  9. Facebook Login
    1. Prerequisite: Ensure you have a Facebook Developer Account (see link below).
    2. Create a Facebook app in https://developers.facebook.com/.
      1. Registration steps:
        1. Login or create Facebook account.
        2. Facebook for Developers, Welcome page, click Continue.
        3. Verify account using mobile number.
        4. Contact info email confirmation.
        5. Choose About you option and Complete Registration.
      2. Meta for Developers dashboard:
        1. If you do not yet have an app, click Create App.
          1. Select Consumer, click Next.
          2. Basic information screen:
            1. Add an app name field, input a unique name.
            2. Click Create app button. 
    3. Return to Meta for Developers dashboard.
    4. Add product, Facebook Login, click Set up (hold off on further changes until indicated in step 9(g) below).
    5. Under sidebar Settings→Basic:
      1. Under App domains, set the domain from step-5(n) i.e. https://<your-region>-<project-id>.cloudfunctions.net
      2. Under Privacy Policy URL, set it to your company’s privacy policy URL.
      3. Under User data deletion, set it to your company’s data deletion instructions or callback URL.
      4. Click bottom option, Add platform.
        1. Select Website and add url from step-5(n) under Site URL.
      5. Click on Save changes.
    6. Under Settings→Advanced:
      1. Turn on Native or desktop app?
    7. Under Products→Facebook Login→Settings:
      1. Set Client OAuth login to Yes.
      2. Set Web OAuth login to Yes.
      3. Set Enforce HTTPS to Yes.
      4. Set Use Strict Mode for redirect URIs to Yes.
      5. Set Valid OAuth Redirect URIs to url in step-5(n)-i.
    8. Click on Save changes.
    9. In the top of the window set the App Mode to Live.
    10. Under Settings→Basic keep a note of the App ID and App Secret.
  10. Firebase Facebook Login
    1. Enable Facebook sign-in method in Firebase console:
      1. Go to the Firebase console under Authentication→Sign-in method→Add new provider, choose Facebook to add it into the list of providers.

      2. Under Facebook, set the App ID and App secret from Step-9(j).

         

  11. Firebase Database Rules:
    1. In the Realtime Database section in the Firebase console, under Rules make sure to disable read/write so that no user can access the project but just make authentication requests.

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]]> NLP Service Comparison https://ride.ict.usc.edu/support/documentation/examples/nlp-service-comparison/?utm_source=rss&utm_medium=rss&utm_campaign=nlp-service-comparison Tue, 13 Sep 2022 23:53:12 +0000 https://ride.ict.usc.edu/?page_id=5293 Demonstrates the integration of cloud-based Natural Language Processing services for comparison within a Q & A format.

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Example

NLP Service Comparison

Overview

Demonstrates the integration of cloud-based Natural Language Processing services for comparison within a Q & A format.

Page Contents

Example NLP Image 1
ExampleNLP

Purpose

Shows how to leverage commodity NLP AI services through REST calls, using RIDE’s common web services system.

Leverages RIDE’s generic NLP C# interface, primarily focused on question-answering interactions, with the principle generally working for user input and agent output. Optionally includes sentiment analysis and entity analysis.

Current Providers

  • Microsoft Azure Cognitive Service for Language
  • Amazon (AWS) Lex
  • Google DialogFlow
  • OpenAI GPT-3
  • OpenAI ChatGPT-4

How to Use

IMPORTANT

If you wish to use the ExampleNLP scene and related web services, you must update your local RIDE config file (ride.json). Please back-up any local changes to the file, including your terrain key. Next, restore your Config to default using the corresponding option in the Config debug menu at run-time, then reapply any customization and terrain key. Refer to the Config File page within Support for more information. 

Test the different types of inputs, different lengths of responses, and measure metrics as the average response time for both “hot” and “cold” services.

A service is said to be “cold” when the service requesting the NLP agent makes the very 1st request just after its initialization and deployment and the agent has not been used for a while (here leaving all the respective platform agents idle for >=5 minutes).

When the service requesting the NLP agent has already made very 1st request just after its initialization and deployment, any subsequent service request is considered “hot”.

Single Provider UI

  1. Click Cycle Providers button to cycle through currently available services.
  2. Input text in the bottom field and hit enter or click the Ask button.
  3. The side pane populates with the following information after entering a query:
    1. Response Time
    2. Text Analytics
    3. Entities
  4. If present, a custom or automatic answer will appear between the pane and input field.

Note that most services are domain specific, with just a handful of questions and answers authored, purely for demonstration purposes. Example question: “What is RIDE?” The exception is OpenAI GPT-3, which is a general purpose language model.

Ask the same question and see how the output differs.

Multiple Provider Comparison UI

  1. Click the Compare Providers button to toggle the live, side-by-side comparison interface.
  2. Input text in the bottom field and hit enter or click the Ask button. Any question will be sent to all 3 providers simultaneously.
  3. Each service has its own pane which populates with the following information after entering a query:
    1. Answer
    2. Response Time
    3. Text Analytics
    4. Entities

Scene Location & Name

Assets/Ride/Examples/NLP/ExampleNLP.unity

Setup Requirements 

The ExampleNLP scene utilizes a customizable UI and interchangeable service providers through canvas, scripts and prefabs. Explore the objects in the Hierarchy view for the scene inside the Unity editor and source for Ride.NLP via the API documentation.

The main script is ExampleNLP.cs in the same folder.

Any NLP option implements the INLPQnASystem C# Interface, which itself is based on INLPSystem. These systems provide the main building blocks to interface with an NLP service:

				
					
OpenAIGPT3System m_openAIGPT3;
var openAIGPT3Component = new GameObject("OpenAIGPT3System").AddComponent<OpenAIGPT3System>();
m_openAIGPT3 = openAIGPT3Component;

The specific endpoint and authentication information is defined through RIDE’s configuration system:

				
					
m_configSystems = Globals.api.systemAccessSystem.GetSystem<RideConfigSystem>();
openAIGPT3Component.m_uri = m_configSystems.config.openAI.endpoint;
openAIGPT3Component.m_authorizationKey = m_configSystems.config.openAI.endpointKey;

The NLP system encapsulates the user question into an NLPQnAQuestion or NLPRequest object. This user input is sent through the RIDE common web services system. The results are encapsulated into an NLPQnAAnswer  or NLPResponse object. A provided callback function allows a client (in this case this ExampleNLP) what specifically to do with the result. The NLP system itself handles much of the work, so that the client only requires one line to send the user question: 

				
					
m_openAIGPT3.AskQuestion(userInput, OnCompleteAnswer);

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]]> RIDE ATLAS https://ride.ict.usc.edu/support/documentation/examples/ride-atlas/?utm_source=rss&utm_medium=rss&utm_campaign=ride-atlas Fri, 26 Aug 2022 17:49:31 +0000 https://ride.ict.usc.edu/?page_id=4683 The RIDE ATLAS (Aerial Terrain Line of Sight Analysis System) integration represents an improved package of ATLAS rebuilt within RIDE.

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Example

RIDE ATLAS

Overview

The RIDE ATLAS (Aerial Terrain Line of Sight Analysis System) integration represents an improved package of ATLAS rebuilt within RIDE.

Page Contents

Ride Atlas UX
RideAtlas

Purpose

The RIDE ATLAS (Aerial Terrain Line of Sight Analysis System) integration represents a vision of a complete, better package of ATLAS rebuilt within RIDE.

How to Use

Access ATLAS functionality via the new RIDE button found in the bottom left corner of the UI.

The Home button returns the camera to the default position.

The Unit Builder menus allow users to customize infantry and ordnance BLUFOR and OPFOR units.

Edit any placed unit on the terrain by double-clicking/tapping on the unit or clicking/tapping the Edit (Pencil) icon for the placed unit in the Unit Pool menu.

Modify the unit name in its properties pane once selected from the pool menu.

Agent LOS

  • Individual Blue/Red agents display a LOS radius corresponding to their respective color. This radius will appear obstructed by terrain and structural obstacles. 
  • Where instances of agent LOS radii overlap their colors will intensify.

Overlays

  • MGRS Grid – toggles grid lines
  • Contour Lines – toggles terrain contour lines

Shading

  • Terrain – default visualization
  • Slope – toggle between Green-Red and Panchromatic
  • Elevation – toggle between Green-Red and Desert Mono

Camera

  • Right-click and hold to adjust camera orientation
  • Press WASD keys to move camera
  • Press QE keys to rotate camera
  • Hold Space bar to raise camera
  • Hold Ctrl to lower camera

Scene Location & Name

Assets/Scenes/RideAtlas/RideAtlas.unity

Note

Currently the source files for this scene are not included in our external distribution. This scene is only available in binary format via standalone build. Please contact us to request a build.

Setup Requirements

This scene incorporates many features and systems within RIDE.

RideSystems Prefab Script

Add this prefab to the scene. Under ResourceLoader nested prefab, under Unit Prefabs, add prefabs WaypointVisualizer, LocationMarker and Ordnance.

Terrain-related Prefabs

Instance the following prefabs to enable loading of a navigable terrain map and related features:

  • TerrainCameraLOS prefab – contains mask component for LOS system; replaces MainCamera
  • TerrainCenter object – sets default camera position relative to placement on map at run-time
  • TerrainDestructor prefab – enables simple explosions via mouse click at run-time
  • TerrainLoader prefab – defines the terrain map for loading
  • TerrainNavLoader prefab – unit navigation of terrain
  • TerrainMeasureDistance prefab – Point measurement UI 
  • GeoreferenceLocationMarkerMenu prefab – Enable placement of georeference location markers

RideAtlas Script

The main scene logic script. Link various scripts in the fields as referenced.

Compass Menu Prefab

Enables onscreen compass UI. Note, prefab dependent upon CompassMenu script found in RideAtlas sub-folder. 

Unit Placement Script

Required to instance units on terrain via UI menu.

Canvas and Related Prefabs

Consists of the the various buttons, panels and menus for the UI. 

Known Issues

Multiple known issues exist in this prototype scene currently, with the intention of addressing them with future releases.

  • Unit pathing option not available
  • Cannot remove measurement or georeference markers
  • Units engage prior to placement
  • Error occurs upon death of unit prior to placement
  • Performance degrades with multiple units present; mitigate by limiting onscreen units and reducing LOS value per unit
  • Waypoint lines over terrain may appear jagged 

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]]> Barracuda API https://ride.ict.usc.edu/support/documentation/examples/barracuda-api/?utm_source=rss&utm_medium=rss&utm_campaign=barracuda-api Fri, 25 Mar 2022 22:26:34 +0000 https://ride.ict.usc.edu/?page_id=4430 This example demonstrates how to examine and run unit tests on various Machine Learning models residing within/outside RIDE backed with Unity Barracuda framework.

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Example

Barracuda API

Overview

This example demonstrates how to examine and run unit tests on various Machine Learning models residing within/outside RIDE backed with Unity Barracuda framework

Page Contents

Barracuda API Image 1
ExampleBarracudaAPI

Purpose

This example demonstrates how to examine and run unit tests on various Machine Learning models residing within/outside RIDE backed with Unity Barracuda framework. 

How to Use

Run-time, either inside Unity editor or standalone:

  1. Select the Inference Type from the dropdown list. For this example here choose Classification.
  2. Load the Machine Learning model (as ONNX) into the scene by pressing the Load ONNX Model button. This model can lie within the Unity project or anywhere locally. For this example, in the file manager that pops up, browse to Assets/Ride/ML/BarracudaAPI/Models/Classification and select the classification_efficientnet-lite4-11.onnx file there and click Open.

  3. Load the parameters defining the sizes of various I/O for this model. Click on the Load Parameters button. This file can lie within the Unity project or anywhere locally. For this example, in the file manager that pops up, browse to Assets/Ride/ML/BarracudaAPI/Models/Classification and select the parameters.txt file there and click Open.
  4. Load the labels defining the various classes for the classification. Click on the Load Labels button. This file can lie within the Unity project or anywhere locally. For this example, in the file manager that pops up, browse to Assets/Ride/ML/BarracudaAPI/Models/Classification and select the labels_map.txt file there and click Open.
  5. Select the Input Type from the drop down list. Choose Single Image.
  6. Load in the image input by pressing the Load Input button. This image can lie within the Unity project or anywhere locally. For this example, in the file manager that pops up, browse to Assets/Ride/ML/BarracudaAPI/Models/Classification/Test data and select the water_bottle.png file there and click Open.
  7. Click on the Run Inference button to the result of this Classification model.


Other Use Cases

Classification from live webcam feed:

RIDE MSELI:

 

Scene Location & Name

Assets/Ride/ML/BarracudaAPI/Scenes/ExampleBarracudaAPI.unity

Setup Requirements 

For the Ride MSELI example, make sure to unzip Assets/Ride/ML/BarracudaAPI/Models/MSELI_LSTM/Test data.zip before running the experience.

Note

Currently this API does not work in WebGL mode. Investigating compatibility in a future update.

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]]> Timeline https://ride.ict.usc.edu/support/documentation/examples/timeline/?utm_source=rss&utm_medium=rss&utm_campaign=timeline Wed, 16 Feb 2022 00:29:21 +0000 https://ride.ict.usc.edu/?page_id=4340 Demonstrates how to use Unity’s Timeline package with the RIDE API's mechanics.

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Example

Timeline

Overview

Demonstrates how to use Unity’s Timeline package with the RIDE API’s mechanics.

Page Contents

Timeline Compilation Example
TimelineCompilation

Purpose

Demonstrates how to use Unity’s Timeline package with the RIDE API’s mechanics.

  • TimelineCompilation scene features Timeline’s general functionalities, such as spawning, commanding entities, and controlling cameras.

How to Use

General Setup

  1. Add RideSystems prefab to the scene’s Hierarchy window.
  2. Click on RideSystems and click on Add Component in the Inspector, and then Add Ride Timeline.
  3. Right-click on the Project tab. Click Create/Timeline to create Timeline asset.
  4. Click and drag created Timeline asset to the scene’s Hierarchy tab.
  5. Click on Window/Sequence/Timeline to open a Timeline window.
  6. Clicking on Timeline in the Hierarchy to bring it up on the Timeline window.
  7. Drag RideSystem from the Hierarchy window to the left side of the Timeline window. Select Add Ride Timeline Track.
  8. Right-click on the track marked by blue color. Select desired clip. Alternatively, click and drag script containing desired event into the track.
  9. Click on the clip in the Timeline window. Enter/add the necessary information for the clip in the inspector.
  10. Hit the play button to play the timeline. (This is assuming the Play on Awake option in the Timeline asset is turned on. It is set to be turned on as default.)

Timeline Window Controls

  • Click and drag to move clips across the track to set a time frame for the clip.
  • Click and drag the boundary of the clip to manipulate the duration of the clip.
    • Note: The length of the clip may not represent the actual length of the event as most Timeline clip events are used as signals for starting the event and have no way of knowing when the event would end.
  • Click with scroll wheel in the timeline window to pan around the tracks.
  • Use scroll wheel to zoom in/out of the Timeline window.

Scene Location & Name

Assets/Ride/Examples/Timeline/TimelineCompilation.unity

Setup Requirements 

Tips & Tricks

Grouping 

Right-click on the Timeline window/Track Group to create grouping for Timeline tracks. Drag tracks in/out of the group to organize tracks by category.

Nested Timeline Directors 

Timeline Directors can be nested, but it is not recommended to do so in RIDE as it will limit the functionality such as Timeline Condition Checks. A child Director will follow the time flow of the parent Director, so Conditional Checks will have no effect on the child Director.

Lock Icon

Clicking the lock icon located to the top-right of the Timeline window will hold the currently selected Director from the Hierarchy window in view regardless of subsequent item selection.

Use Gizmo to Adjust Clip Object Position

The transform gizmo will appear in the Scene window for certain Timeline clips, such as for spawning and moving. Click and drag the gizmo’s handles to change the  location/position parameter when editing for easier value manipulation compared to manual entry.

Advanced Topic: Scripting

Timeline in RIDE mostly follows the structure as set by Unity. Please refer to the official documentation for more details:  https://docs.unity3d.com/2018.3/Documentation/ScriptReference/Timeline.TimelineAsset.html

How to Add New Timeline Commands

  1. Create new behavior: Timeline behavior can be thought of as data storage for Timeline events. Create a class for a new command’s behavior, deriving from RideTimelineBehaviour, and add a list of variables that is needed for the new command.
  2. Create new clip: Create a class deriving from RideTimelineClip.
    1. Declare type of behavior needed for the clip.
    2. Set value for shouldExecuteOnce. Set to false if command needs to be called every frame to be updated.
    3. Create new enum to RideTimeline.TimelineCommandType. Declare it inside class for new clip.
    4. Declare variables for the clip. The variable needs to be declared with exposed reference if object in the scene needs to be referenced.
    5. Override CreatePlayable(PlayableGraph, GameObject).
      1. Create a ScriptPlayable with matching behavior type.
      2. Assign variables declared in clip with matching variables from behavior.
      3. Exposed variable needs to be resolved.
      4. Return ScriptPlayable.
    6. Write logic for command in Execute Behavior(RideTimelineBehaviour).
      1. Cast behavior to matching type for the clip.
      2. Utilize data set in clip from the behavior.
  3. Go to ExecuteCommand() in the RideTimline.cs. Write case for new clip with TimelineCommandType as a condition.
  4. Create new clip in the Timeline window to execute command.

Clip Index

Agent

Clip_AgentMoveTo

Description: Command agent to move to location.

Parameters:

Move to Location: position agent will be commanded to move to. Can be manipulated with transform handle gizmo in the Scene window.

Move to Object: position of the object agent will be commanded to move to. Using this parameter will make Move to Location to be ignored. Activate by dragging an object from Hierarchy window to it.

Agent Name: Name of the agent that command is issued out to.

Note: An agent with a matching Agent Name must exist in the scene.

Clip_AgentSetPosture

Description: Command agent to change posture.

Parameters:

Posture: Type of posture.

Agent Name: Name of the agent that command is issued out to.

Note: An agent with a matching Agent Name must exist in the scene.

Clip_AgentShootAgent

Description: Command agent to shoot another agent.

Parameter:

Agent Name: Name of the agent commanded to shoot.

Target Name: Name of the agent that will be shot.

Note: Agents with Agent Name and Target Name must exist in the scene.

Clip_AgentSpawn

Description: Command agent to be spawned in the scene.

Parameters:

Spawn Location: Position in the world where an agent will be spawned in.

Spawn Rotation: Rotation of the agent will be spawned in.

Target Speed: Value of agent’s target speed.

Max Speed: Value of agent’s max speed.

Skill Level: Skill Level of the agent.

Team Color: Agent’s team.

Agent Name: Name of the agent.

Note: Spawn Location must be a valid position in the world.

Group

Clip_GroupAmbushInitiate

Description: Command group of agents to initiate an ambush. (https://ride.ict.usc.edu/support/documentation/examples/battle-drills-react-to-ambush-near/)

Parameters:

Group Name: Name of the group command is issued out to.

Note: Group with Group Name must be present in the scene. Another group of agents must be given a command to react to ambush. Region prefab must be present in the scene.

Clip_GroupAmbushReact

Description: Command group of agents to walk into position in the world, where they will walk and react to ambush. (https://ride.ict.usc.edu/support/documentation/examples/battle-drills-react-to-ambush-near/)

Parameters:

Move to Location: Position in the world where the group will move to.

Group Name: Name of the group command is issued out to.

Note: Group with Group Name must be present in the scene. Another group of agents must be given a command to react to ambush. Region prefab must be present in the scene. Agents must walk inside the region to initiate ambush event.

Clip_GroupMoveInFormation

Description: Command group of agents to move in formation.

Parameters:

Move to Location: Position in the world where the group will move to.

Move to Object: position of the object agent will be commanded to move to. Using this parameter will make Move to Location to be ignored. Activate by dragging an object from the Hierarchy window to it.

Formation Type: Type of formation group will move in.

Group Name: Name of the group command is issued out to.

Note: Group with Group Name must be present in the scene.                     

Clip_GroupSpawnFireTeam

Description: Command group of agents with a size of fire team to be spawned in the scene.

Parameters:

Spawn Location: Position in the world where a fire team will be spawned in.

Spawn Rotation: Rotation of the fireteam that will be spawned in.

Team Color: Fireteam’s team.

Agent Name: Name of the group.

Note: Spawn Location must be a valid position in the world.

Camera

Clip_CameraDisable

Description: Disable camera.

Parameters:

Camera: Camera that command is given out to.

Note: Camera parameter cannot be null.

Clip_CameraEnable

Description: Enable camera.

Parameters:

Camera: Camera that command is given out to.

Note: Camera parameter cannot be null.

Clip_CameraFollowEntity

Description: Command camera to follow an entity in the scene.

Parameters:

Camera: Camera that command is given out to.

Entity Name: Name of the entity that camera will follow.

Camera Offset: Vector value that camera will be away from the entity.

Camera Rotation: Rotational value of the camera.

Focus on Agent: Checking this option will override Camera Rotation and camera will focus on the entity.

Note: 

Camera parameter cannot be null.

There must be entity with matching Entity Name must exist in the scene.

The length of the clip determines the duration of the command.

Clip_CameraFollowTrack

Description: Camera will move along the track/curve in the scene.

Parameters:

Camera: Camera that command is given out to.

Curve: Curve that camera will follow.

Travel Duration: How long it will take for the camera to finish following the track.

Note:

Camera and Curve parameter cannot be null.

Travel Duration does not guarantee the duration of the clip. Length of the clip must match the Travel Duration to guarantee the camera to finish travelling the track from start to finish.

Object with VhAssetsBezierCurveSystem component must be present in the scene. This object must be added to Curve System parameter inside the RideTimeline component in the RideSystems prefab.

Curve must be present in the scene.

Clip_CameraFOVSet

Description: Set field of view (FOV) of the camera.

Parameters:

Camera: Camera that command is given out to.

FOV: Value of FOV that camera will set to.

Note: Camera parameter cannot be null.

Clip_CameraLerpToPosition

Description: Lerp Camera’s current position + rotation to another position + rotation.

Parameters:

Camera: Camera that command is given out to.

Target Position: Position in the world that camera will move to.

Camera Rotation: Rotational vector that camera will rotate to.

Speed: How fast this command is carried out.

Note:

Camera parameter cannot be null.

Length of the clip determines the duration of the command.

Clip_CameraLookAtEntity

Description: Camera will focus on the entity in stationary position.

Parameters:

Camera: Camera that command is given out to.

Entity Name: Name of the entity that camera will focus on.

Note:

Camera parameter cannot be null.

Length of the clip determined the duration of the command.

Clip_CameraLookAtPosition

Description: Camera will look at given position in stationary position.

Parameters:

Camera: Camera that command is given out to.

Target Position: Name of the entity that camera will focus on.

Note: Camera parameter cannot be null.

Clip_CameraMoveTo

Description: Camera will teleport to given position in the world.

Parameters:

Camera: Camera that command is given out to.

Position: Position in the world where camera will teleport to.

Rotation: Rotational value that camera will be set to.

Note:

Camera parameter cannot be null.

Activating this command will not turn the camera on.

Clip_CameraSwap

Description: Turn off current camera and turn on the camera set in the parameter.

Parameters:

Camera: Camera that will be turned on.

Note:

Camera parameter cannot be null.

Game Object

Clip_ObjectFadeOut

Description: Fade out object’s material until it becomes transparent.

Parameters:

Renderer List: List of objects that command will be issued out to.

Note: Material and shader of the object may prevent this command from being performed effectively.

Clip_ObjectSpawnRegion

Description: Spawn region object in the scene.

Parameters:

Spawn Location: Position in the world where region will be spawned in.

Scale: Scalar value for region’s size.

Note:

This command is essential part of setting up ambush behavior.

Only agents that were spawned before region is spawned will be processed for ambush behavior.

BD_GenerickKillZone prefab must be added to Region Prefab parameter inside the Ride Timeline component in the RideSystems prefab.

Mount

Clip_MountAimAt

Description: Command mount to aim at position/object in the scene.

Parameter:

Target Object: Command mount to aim at this object.

Target Location: Command mount to aim at this position.

Mount Name: Name of the mount command is issued out to.

 Note:

Using Target Object parameter will override Target Location.

Aiming process may take some mount to take time to finish.

Mount with matching Mount Name must be present in the scene.

Clip_MountFireAt

Description: Command mount to fire at position/object in the scene.

Parameter:

Target Object: Command mount to fire at this object.

Target Location: Command mount to fire at this position.

Mount Name: Name of the mount command is issued out to.

Note:

Using Target Object parameter will override Target Location.

Projectile may miss the target if aiming is unfinished.

Mount with matching Mount Name must be present in the scene.

Destructible objects must be added to RideTimeline component inside RideSystems prefab’s RideTimeline component to utilize destructible object.

Clip_MountMoveTo

Description: Command mount to move to given position/object in the scene.

Parameter:

Move to Object: Command mount to move to this object’s position.

Move to Location: Command mount to move to this position.

Mount Name: Name of the mount command is issued out to.

Note:

Using Move to Object parameter will override Move to Location.

Mount with matching Mount Name must be present in the scene.

Clip_MountSpawn

Description: Spawn mount in the scene

Parameter:

Spawn Location: Position in the world where mount will spawn in.

Spawn Rotation: Rotational value that mount will spawn in.

Mount Type: Type of mount that will be spawned.

Mount Name: Name of the mount that will be used as ID.

Note: Spawn Location must be valid value within the scene.

Utility

Clip_UtilTurnOffAutoFight

Description: Turn off auto fight flag in the scene. Agents with opposing faction will not automatically start combat.

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