NASA · NASA-543 · record R0280

Aerial AId: Visual data processing with AI in emergency scenarios for quick decision making

National Aeronautics and Space Administration · LaRC: Langley Research Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Emergency Management
Operational functions: Image and video analysis Derived by keyword rules; see methodology

Problem the AI is intended to solve

The Aerial AId activity enables the use of perception engines – primarily classification and object detection algorithms – in small uncrewed aerial systems (sUAS) for sUAS medical emergency response service providers. The goal of Aerial AId is to demonstrate a prototype optimized and trained perception engine, application specific datasets, and assurance framework.

Expected benefits

This work will specifically enable stakeholders in the commercial sUAS sector that are well positioned to develop sUAS concepts for emergency medical response applications.

System outputs

This project aims to provide the initial steps to safely enabling artificial intelligence/machine learning (AI/ML) technology for aerial emergency medical response with the potential to drastically improve emergency medical operations by increasing efficiency, reducing mission time, and reducing the strain on humans involved in the operations. The vision for a future fully automated capability addresses the primary challenge to adoption for sUAS into the emergency medical response sector, and this technology development overlaps significantly with ARMD goals by creating a framework for assured autonomy.

Technology

AI classification: Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported