Intelligent Contingency Management

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

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Adapt and train AI algorithms to contribute to an autonomous vehicle mission manager for Advanced Air Mobility (Cargo, Air Taxis). At a high level, the AI must recognize contingency flight conditions and react appropriately to return the aircraft to safe flight status. The project has three main objectives: 1. Explore machine learning for intelligent contingency management, with a focus on assessing/projecting vehicle capability and maintaining nominal performance via reinforcement learning. 2. Develop vehicle intelligent contingency management system architecture at a functional level and validate against a specific Unmanned Air Mobility (UAM)-class vehicle. 3. Incorporate (1) and (2) into an evolving toolset for an autonomous vehicle.

Expected benefits

Adapt and train AI algorithms to contribute to an autonomous vehicle mission manager for Advanced Air Mobility (Cargo, Air Taxis). At a high level, the AI must recognize contingency flight conditions and react appropriately to return the aircraft to safe flight status. The project has three main objectives: 1. Explore machine learning for intelligent contingency management, with a focus on assessing/projecting vehicle capability and maintaining nominal performance via reinforcement learning. 2. Develop vehicle intelligent contingency management system architecture at a functional level and validate against a specific Unmanned Air Mobility (UAM)-class vehicle. 3. Incorporate (1) and (2) into an evolving toolset for an autonomous vehicle.

System outputs

Outputs include recognition of off-nominal conditions (contingencies) and mission executon strategy adjustments.

Technology

AI classification

Agentic AISource: Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.

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.

Potential impacts and how they were identified

Not reported