Frequently Asked Questions
Updated: March 25, 2021
RIDE is a simulation environment that unites many DoD simulation efforts to provide an accelerated development platform and prototyping sandbox that will provide direct benefit to the Army’s Synthetic Training Environment (STE) as well as the larger DoD simulation communities.
- A Suite of Capabilities: Has simple integrations, drag and drop in many cases.
- Built agnostically: Currently supports Unity – coded to enable straightforward ports to additional runtimes such as Unreal.
- A Single Codebase: Enables sharing complex capabilities easily.
- Written Once: Can be reused over and over.
- Various Capabilities: Can be added, optimized, and iterated upon for the use of all users.
Planned future capabilities and support:
- Computer Vision: Expression Recognition
Web Services(Now supported via LMCO implementation!)
- Advanced Logging & Curation
- Natural Language Processing
- Portable Containerized functionality
- Unreal Engine support
- Full suite of AR/VR capabilities and interfaces such as Eye Tracking
- Experiment Builder for Human Research
- Potentially more from in-progress partnerships!
Sponsors & Collaborators:
- U.S. Army Synthetic Training Environment (STE)
- U.S. Army Network Cross-Functional Team (N-CFT)
- U.S. Army Simulation and Training Technology Center (STTC)
- Army Research Laboratory (ARL)
- DEVCOM Soldier Center
- DEVCOM Ground Vehicle Systems Center
- Dignitas Technologies
- Engineer Research and Development Center (ERDC)
- Lockheed Martin (LMCO)
- MITRE Corporation
- Naval Information Warfare Center (NIWC) Pacific
- Naval Surface Warfare Center (NSWC)
- Office of Naval Research (ONR)
- UCT IST, School of Modeling Simulation and Training
- Unity Technologies
- USC Institute for Creative Technologies
- Additional collaborators in-progress!
Capabilities integrated by ICT’s STE Research Projects:
- One World Terrain Data and Tools
- Training Simulation Software Compatibility
- Speech Recognition
- Event Injection & Narrative Summarization
- Cognitive Behavior Modeling
- Native Machine Learning Foundation
- Logging, including xAPI and Learner Records
- Patterns of Life and Realistic AI Behaviors
- Contributions from other projects in-progress!