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CANA Independent Research & Development (IRAD)

Updated: Nov 28, 2023

Live, Virtual, and Constructive Simulation (LVC Sim)

by Chris Cichy

The vision of this IRAD is to utilize a game engine that supports modifications and scripting to develop Live, Virtual, and Constructive (LVC) training environments to support concept, doctrine, and requirements development. LVC Simulation (LVC Sim) training has multiple markets within and outside of the Department of Defense; it is applicable to any agency in which training is cost or effort prohibitive, in this case, interagency firefighting. LVC Sim, built on an open-world gaming engine, would allow real-time wargaming capability with a minimal investment and could be made playable by a large audience to increase the sample size and remove currently existing stovepipes.

Playable demonstrations and videos can be made to visualize and pitch new technology and concepts and could be used to develop new marketing tools for a customer’s requirements. For this particular IRAD, CANA could augment its game modification experience through a proposed innovation challenge, or “hackathon.” The goal of this hackathon would be to modify one virtual twin of a technology from an automated firefighting research and development project led by partners NIWC Pacific and the NavalX SoCal Tech Bridge called Project Vesta.

With this head start in development and the experience gained by working with the innovation challenge participants, CANA would develop each of the assets from Project Vesta and create playable use cases for the pilot. Randomizing certain variables within the game world would then allow for a Project Vesta specific virtual training system.

The long-range potential is limitless. Follow-on exploration might include producing a virtual training environment for autonomous vehicle software; developing tactics and doctrine lesson plans as modification packs; synthetic training data for Artificial Intelligence (AI) models; or developing synthetic environments with integrated Generative Adversarial Networks (GAN) for the training of deep learning models. They are ideas on the cutting edge, and CANA is excited by what our team will think up next!

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