Intelligent Contingency Management
Description
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.
Detailed example
Outputs include recognition of off-nominal conditions (contingencies) and mission executon strategy adjustments.
AI / analytics pattern
Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
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.
Controls / human review
ATO: Not reported; PIA: Not published