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Directorate for Computer and Information Science and EngineeringNSF · NSFNSF

Collaborative Research: CISE-MSI: RCBP-RF: CPS, CNS: Emergency Response and Evacuation Training for Active Shooter Events

Sharad Sharma·University of North Texas, TX·2022–2025·COMPLETED
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INSTITUTION

University of North Texas, TX

PRINCIPAL INVESTIGATOR

Sharad Sharma

FUNDING

$180K

YEAR

2022

MOONBASE SCORE

1/100

LOADING MOONBASE SCORE

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). In recent years, we have witnessed a sharp increase in active shooter events; however, unfortunately, there has been no introduction of new technology or tactics capable of increasing preparedness and training for active shooter events. This project promotes the progress of science and technology development by building interdisciplinary partnerships and research capacity between Bowie State University (BSU) and University of Nevada, Las Vegas (UNLV) for exploratory investigation of active shooter events at Institutes of Higher Education (IHE). The goal of this project is to conduct data collection, preliminary experiments, and a prototype development for the two campuses for improving emergency preparedness for an active shooter event using collaborative immersive virtual reality (VR) environment. This project develops algorithms, implement software and demonstrate proof-of-concept using building systems as a challenge application area. This research involves devising novel conceptual methods for new theoretical and empirical guidance needed to have an environment for human and machine decision making for highly uncertain, complex, time urgent, and dynamically changing missions. The research objectives of this proposal include: 1) Develop an immersive Collaborative Virtual Environment (CVE) using Oculus for course of action, visualization, and situational awareness for active shooter events, 2) Develop a unique immersive graphical user interface (GUI) for communicating between PCs (Player characters) and NPCs (Non-Player Characters) for user interaction, 3) Implementing algorithms for human behavior for NPCs in active shooter events. (e.g. Hostile, Non-Hostile, Selfish, Leader-Following, Panic), 4) Deep learning-based (data-driven) behavioral modeling for realistic NPCs. The successfully implemented project contributes to the state-of-the-art in emergency response training using VR by developing knowledge that could form the basis of public education and awareness programs to help facility occupants, their rescuers and city officials respond appropriately during extreme emergency situation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Directorate for Computer and Information Science and EngineeringCOVID-Disproportionate Impcts Inst-IndivCISE MSI Research ExpansionchallengeworthyreflectssituationintroductionyearsduringcouldurgentrecentDeep LearningVirtual RealityActive Shooter EventsEmergency Response TrainingCollaborative Virtual EnvironmentImmersive GUIBehavioral ModelingHuman-Machine InteractionSituational AwarenessNPC Behavior

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