Using Artificial Neural Networks for Predicting Vehicle Survivability within a Virtual Simulation U.S. DEVCOM GVSC

Format:

Book

Conference/Event

Author/Creator:

O'Bruba, Joseph, author.

Conference Name:

2025 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium (2025-08-12 : Novi, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

A unique contribution the U.S. Army currently provides is what is known as Virtual Experiments (VEs). A VE consists of a large group of active-duty soldiers who participate in a video game simulating a battlefield scenario. During these simulations, the soldiers are provided with novel protective vehicle capabilities in an effort to evaluate their effectiveness on the battlefield. However, these VEs take a significant amount of time to conduct and are expensive. Using Artificial Neural Networks (ANNs) this study looks to predict vehicle survivability based on a limited amount of VE data. The results entail an overall predictive accuracy of 76.8% using only two ANN input features and provides a framework for the eventual addition of more VE datasets

Notes:

Vendor supplied data

Publisher Number:

2025-01-0482

Access Restriction:

Restricted for use by site license