Example Topic Archives - RIDE https://ride.ict.usc.edu/category/example-topic/ Rapid Integration & Development Environment Tue, 28 Apr 2026 16:41:03 +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 Topic Archives - RIDE https://ride.ict.usc.edu/category/example-topic/ 32 32 Virtual Humans https://ride.ict.usc.edu/support/documentation/examples/virtual-humans/?utm_source=rss&utm_medium=rss&utm_campaign=virtual-humans Thu, 01 Sep 2022 21:41:21 +0000 https://ride.ict.usc.edu/?page_id=4990 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Virtual Humans

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Example Face Image 2

Audio-Visual Sensing

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

Example NLP Image 1

NLP Service Comparison

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

Example State Machine Image

State Machine

Basic Agent state machine that can be modified as needed for simulations.

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]]> Terrain https://ride.ict.usc.edu/support/documentation/examples/terrain/?utm_source=rss&utm_medium=rss&utm_campaign=terrain Thu, 01 Sep 2022 21:40:43 +0000 https://ride.ict.usc.edu/?page_id=4988 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Terrain

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Example Ground Classification Weather Image 00

Ground Classification Weather

Demonstrates the dynamic navigation of different unit types to an arbitrary waypoint under a severe weather event affecting ground conditions.

Example LOS Image

Line of Sight

Demonstrates terrain map line-of-sight (LOS) system for BLUEFOR, OPFOR and CIV agents. 

Razish Tour Image

Razish Tour

This example scene shows how to create an interactive tour around a terrain using scenario events and custom AI behaviors.

Ride Atlas UX

RIDE ATLAS

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

Team Match Room Image

Team Match

Red vs Blue networked competitive multiplayer on various terrain maps with dismounted or vehicular units.

Example Terrain Destruction Image

Terrain Destruction

Apply explosive ordnance of user-set intensity and size at any location on a terrain map. Deformation of the surface geometry may change depending on the material/structure classification at that point.

OWT USC Campus Image

Terrain Loading

Examine available terrain maps in TILE or LMAB formats, selecting between different LOD settings.

Example Trees Image

Trees

Collects the available SpeedTree assets that will be used to make asset bundles for different platforms.

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]]> Logging & Data https://ride.ict.usc.edu/support/documentation/examples/logging-and-data/?utm_source=rss&utm_medium=rss&utm_campaign=logging-and-data Thu, 01 Sep 2022 21:40:14 +0000 https://ride.ict.usc.edu/?page_id=4986 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Logging & Data

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Example DIS Image 01

DIS Messages

Demonstrates how to use RIDE systems to send and receive Distributed Interactive Simulation (DIS) messages.

Example Interpretation System Image 00

Interpretation System

Demonstrates the systems for real-time interpretation of scenario events and narrative summarization.

Example Localization Image 02

Localization

Demonstrates how to load, display, and switch between different languages

RIDE Logo Placeholder

Logging

Use the RIDE API to implement your own logging.

Example MSELI Image

MSELI

Master scenario events list injection (MSELI) allows you to create arbitrary events in the simulation and provide them with execution orders and conditions for their execution during the scenario.

Example OAuth Page

OAuth

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

Example Region System Image

Region System

Track the movement of individual agents and groups as they move in and out of defined polygonal regions.

Example Rest Image

REST Services

Demonstrates how to host a RESTful service in RIDE so that it can be accessed via an HTTP request from a web browser.

Example Sockets Image

Sockets

Demonstrates how to open, send data over, and close a TCP socket in RIDE.

Unit Data Example Image

Unit Data

Save agents and/or groups for later retrieval, allowing them to be loaded into the scene via custom menus.

Example VGL Stress Test Image

VGL Stress Test

Configure a scalability test with generic in-game agents in real-time and record performance data locally and in the cloud for later analysis.

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]]> Programming & Scripting https://ride.ict.usc.edu/support/documentation/examples/programming-and-scripting/?utm_source=rss&utm_medium=rss&utm_campaign=programming-and-scripting Thu, 01 Sep 2022 21:39:43 +0000 https://ride.ict.usc.edu/?page_id=4984 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Programming & Scripting

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Example Agent Behaviours Image

Agent Behaviours

Demonstrates a code-driven set of general and custom agent behaviors for a unit.

Example Agent Commands Image

Agent Commands

Command agents with basic real-time strategy (RTS) control type input. Displays a selector marquee in a specified color, with selected agents displaying a circular icon at their base.

Example Combat Image

Combat

Define the unit spawn parameters of opposing Blue and Red agents. Autonomous behaviors enable each side to navigate obstacles and engage via line-of-sight in order to reach a waypoint.

Dynamic Spawn Example Image

Dynamic Spawn

Spawn individual agents or squads, and then commandeer any single unit with full first-person fire/movement controls.

RIDE Logo Placeholder

Headless Mode

If the CommandLineParser.cs script is in the scene, and you create a build of the scene, then you can process all the ScenarioParameters and run your simulation with or without graphics.

Example Image Generation Capture

Image Generation Comparison

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

Example LOS Image

Line of Sight

Demonstrates terrain map line-of-sight (LOS) system for BLUEFOR, OPFOR and CIV agents. 

RIDE Logo Placeholder

Logging

Use the RIDE API to implement your own logging.

Example MSELI Image

MSELI

Master scenario events list injection (MSELI) allows you to create arbitrary events in the simulation and provide them with execution orders and conditions for their execution during the scenario.

Example OAuth Page

OAuth

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

Razish Tour Image

Razish Tour

This example scene shows how to create an interactive tour around a terrain using scenario events and custom AI behaviors.

Example Rest Image

REST Services

Demonstrates how to host a RESTful service in RIDE so that it can be accessed via an HTTP request from a web browser.

Example State Machine Image

State Machine

Basic Agent state machine that can be modified as needed for simulations.

Team Match Room Image

Team Match

Red vs Blue networked competitive multiplayer on various terrain maps with dismounted or vehicular units.

Example Terrain Destruction Image

Terrain Destruction

Apply explosive ordnance of user-set intensity and size at any location on a terrain map. Deformation of the surface geometry may change depending on the material/structure classification at that point.

OWT USC Campus Image

Terrain Loading

Examine available terrain maps in TILE or LMAB formats, selecting between different LOD settings.

Example Time Simulation capture

Time Simulation

Demonstrates how to control simulation time speed and unit movement speed.

Unit Data Example Image

Unit Data

Save agents and/or groups for later retrieval, allowing them to be loaded into the scene via custom menus.

Example Input Vehicle Image CTR

Vehicles Input

Navigate terrain maps with “drivable” vehicles through basic keyboard or gamepad input. Cycle multiple camera angles in and around the vehicle for observation of variables in the environment that affect physics of the vehicles. An overlay displays vehicle motion statistics in real-time.

Example VGL Stress Test Image

VGL Stress Test

Configure a scalability test with generic in-game agents in real-time and record performance data locally and in the cloud for later analysis.

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]]> Networking & Messaging https://ride.ict.usc.edu/support/documentation/examples/networking-and-messaging/?utm_source=rss&utm_medium=rss&utm_campaign=networking-and-messaging Thu, 01 Sep 2022 21:36:39 +0000 https://ride.ict.usc.edu/?page_id=4978 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Networking & Messaging

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Example Face Image 2

Audio-Visual Sensing

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

Example DIS Image 01

DIS Messages

Demonstrates how to use RIDE systems to send and receive Distributed Interactive Simulation (DIS) messages.

Example Image Generation Capture

Image Generation Comparison

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

Example MSELI Image

MSELI

Master scenario events list injection (MSELI) allows you to create arbitrary events in the simulation and provide them with execution orders and conditions for their execution during the scenario.

Example Rest Image

REST Services

Demonstrates how to host a RESTful service in RIDE so that it can be accessed via an HTTP request from a web browser.

Simple Network Example Image 00

Simple Network Controls

This example demonstrates how to set up a network environment with controllable characters using the RIDE API and Photon asset package.

Example Sockets Image

Sockets

Demonstrates how to open, send data over, and close a TCP socket in RIDE.

Team Match Room Image

Team Match

Red vs Blue networked competitive multiplayer on various terrain maps with dismounted or vehicular units.

Example Web Services Image 03

Web Services

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

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]]> Machine Learning https://ride.ict.usc.edu/support/documentation/examples/machine-learning/?utm_source=rss&utm_medium=rss&utm_campaign=machine-learning Thu, 01 Sep 2022 21:35:31 +0000 https://ride.ict.usc.edu/?page_id=4974 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Machine Learning

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Barracuda API Image 1

Barracuda API

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

Example Behaviour Comparison Image

Behaviour Comparison

This scene demonstrates how one might analyze side-by-side the performance of two approaches for agent cover behaviour: scripted vs. Machine Learning (ML).

Example Localization Image 02

Localization

Demonstrates how to load, display, and switch between different languages

Example Training Take CoverML Image

Training TakeCover ML

Rapid training of mlAgent units for modelling cover behaviour by inference. This scene links to an external python application over Unity TCP containing user-defined observations created in an academy for which the unit “brains” undergo training.

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]]> Behaviors & Navigation https://ride.ict.usc.edu/support/documentation/examples/behaviors-and-navigation/?utm_source=rss&utm_medium=rss&utm_campaign=behaviors-and-navigation Thu, 01 Sep 2022 21:32:20 +0000 https://ride.ict.usc.edu/?page_id=4968 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Behaviors & Navigation

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Example Astar Pathing Image

A* Pathing

Demonstrates how to use the “A* Pathfinding” option as a new movement system type for units.

Example Agent Behaviours Image

Agent Behaviours

Demonstrates a code-driven set of general and custom agent behaviors for a unit.

Example Behaviour Comparison Image

Behaviour Comparison

This scene demonstrates how one might analyze side-by-side the performance of two approaches for agent cover behaviour: scripted vs. Machine Learning (ML).

Example Patrol And Attack Bot Image

Patrol and Attack Bot

Demonstrates autonomous agents using behavior trees (BT) in order to make decisions about patrolling and attacking.

Razish Tour Image

Razish Tour

This example scene shows how to create an interactive tour around a terrain using scenario events and custom AI behaviors.

Example Region System Image

Region System

Track the movement of individual agents and groups as they move in and out of defined polygonal regions.

Ride Atlas UX

RIDE ATLAS

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

Example State Machine Image

State Machine

Basic Agent state machine that can be modified as needed for simulations.

Example Training Take CoverML Image

Training TakeCover ML

Rapid training of mlAgent units for modelling cover behaviour by inference. This scene links to an external python application over Unity TCP containing user-defined observations created in an academy for which the unit “brains” undergo training.

Example Input Vehicle Image CTR

Vehicles Input

Navigate terrain maps with “drivable” vehicles through basic keyboard or gamepad input. Cycle multiple camera angles in and around the vehicle for observation of variables in the environment that affect physics of the vehicles. An overlay displays vehicle motion statistics in real-time.

The post Behaviors & Navigation appeared first on RIDE.

]]> Architecture https://ride.ict.usc.edu/support/documentation/examples/architecture/?utm_source=rss&utm_medium=rss&utm_campaign=architecture Thu, 01 Sep 2022 21:30:23 +0000 https://ride.ict.usc.edu/?page_id=4966 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Architecture

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

RIDE Logo Placeholder

Headless Mode

If the CommandLineParser.cs script is in the scene, and you create a build of the scene, then you can process all the ScenarioParameters and run your simulation with or without graphics.

Example State Machine Image

State Machine

Basic Agent state machine that can be modified as needed for simulations.

The post Architecture appeared first on RIDE.

]]> Agents & Entities https://ride.ict.usc.edu/support/documentation/examples/agents-and-entities/?utm_source=rss&utm_medium=rss&utm_campaign=agents-and-entities Sat, 27 Aug 2022 00:28:37 +0000 https://ride.ict.usc.edu/?page_id=4841 Example scenes pertaining to a set of related RIDE API capabilities.

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Example Topic

Agents & Entities

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Example Agent Behaviours Image

Agent Behaviours

Demonstrates a code-driven set of general and custom agent behaviors for a unit.

Example Agent Commands Image

Agent Commands

Command agents with basic real-time strategy (RTS) control type input. Displays a selector marquee in a specified color, with selected agents displaying a circular icon at their base.

RIDE Asset Lineup Soldiers

Asset Lineup

Collects available entity prefabs for inspection, modification and testing.

Example Behaviour Comparison Image

Behaviour Comparison

This scene demonstrates how one might analyze side-by-side the performance of two approaches for agent cover behaviour: scripted vs. Machine Learning (ML).

Example Combat Image

Combat

Define the unit spawn parameters of opposing Blue and Red agents. Autonomous behaviors enable each side to navigate obstacles and engage via line-of-sight in order to reach a waypoint.

Example Movement Data Mono Image

DataMono Entities

“DataMono” characters built by using bootstrappable data components for entities.

Dynamic Spawn Example Image

Dynamic Spawn

Spawn individual agents or squads, and then commandeer any single unit with full first-person fire/movement controls.

Example Interpretation System Image 00

Interpretation System

Demonstrates the systems for real-time interpretation of scenario events and narrative summarization.

Example LOS Image

Line of Sight

Demonstrates terrain map line-of-sight (LOS) system for BLUEFOR, OPFOR and CIV agents. 

Example Patrol And Attack Bot Image

Patrol and Attack Bot

Demonstrates autonomous agents using behavior trees (BT) in order to make decisions about patrolling and attacking.

Razish Tour Image

Razish Tour

This example scene shows how to create an interactive tour around a terrain using scenario events and custom AI behaviors.

Example Region System Image

Region System

Track the movement of individual agents and groups as they move in and out of defined polygonal regions.

Team Match Room Image

Team Match

Red vs Blue networked competitive multiplayer on various terrain maps with dismounted or vehicular units.

Example Time Simulation capture

Time Simulation

Demonstrates how to control simulation time speed and unit movement speed.

Example Training Take CoverML Image

Training TakeCover ML

Rapid training of mlAgent units for modelling cover behaviour by inference. This scene links to an external python application over Unity TCP containing user-defined observations created in an academy for which the unit “brains” undergo training.

Unit Data Example Image

Unit Data

Save agents and/or groups for later retrieval, allowing them to be loaded into the scene via custom menus.

Example Input Vehicle Image CTR

Vehicles Input

Navigate terrain maps with “drivable” vehicles through basic keyboard or gamepad input. Cycle multiple camera angles in and around the vehicle for observation of variables in the environment that affect physics of the vehicles. An overlay displays vehicle motion statistics in real-time.

Example VGL Stress Test Image

VGL Stress Test

Configure a scalability test with generic in-game agents in real-time and record performance data locally and in the cloud for later analysis.

The post Agents & Entities appeared first on RIDE.

]]> Cinematics & Media https://ride.ict.usc.edu/support/documentation/examples/cinematics-media/?utm_source=rss&utm_medium=rss&utm_campaign=cinematics-media Sat, 09 Jul 2022 05:45:22 +0000 https://ride.ict.usc.edu/?page_id=4656 Audio, cameras, playback, recording, timeline, etc.

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Example Topic

Cinematics & Media

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Example Audio Image

Audio

This example demonstrates how to load and play audio clips in RIDE.

Example Face Image 2

Audio-Visual Sensing

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

Example Camera Home Image 00

Camera Home

Demonstrates how to reset a camera’s position and rotation at run-time using RIDE’s ICamera system.

Example Camera Tween Image

Camera Tween

Demonstrates how to use RIDE’s tween (in-betweening) system to move a camera.

Example Image Generation Capture

Image Generation Comparison

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

Example Particle System Image

Particle System

Displays how to create particle systems from prefabs, scene objects, resources, and RideIDs.

Timeline Compilation Example

Timeline

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

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