FAA Aviation Modernization: A Human-Centric AI Retrofit (HAH)

Current Stage: Transitioning from TRL 2 to TRL 3 (Experimental Proof of Concept) Strategic Objective: To demonstrate a trustworthy aviation operating environment where aircraft, aircraft-mounted sensors, peer aircraft, and ground systems form a cooperative operational network that identifies and propagates operational significance before situations become tactically urgent.

1. The Imperative for a "Third Way" (The Why)

The National Airspace System (NAS) faces an "iron logic" hurdle: 37% of air traffic control systems are unsustainable, yet full-scale infrastructure replacement is a multi-decade, high-cost endeavor. This project proposes a software-first, pragmatic retrofit using Commercial Off-The-Shelf (COTS) hardware to provide immediate relief. This "Third Way" focuses on de-stressing the system through focused augmentation that respects situational authority and human intent.

2. Operational Philosophy: The Non-Event Principle (The What)

Safety is shifted from reactive, last-second intervention to early-stage coordination. The system doesn't merely aggregate data; it determines which changes in the shared airspace picture have operational significance for a particular human.

  • Human Primacy: AI serves strictly as an assistant; human authority remains the final decision-maker. The objective is to reduce information acquisition, sorting, and correlation workload—not to transfer operational decision authority from the human to the AI.

  • The Operational Loop: Observe → Assess → Propagate → Inform → The human decides.

  • The Four-Question Relevance Model: To minimize cognitive noise, the system continuously evaluates:

    1. What changed? (Environmental/Internal sensing)

    2. Who could be affected? (Impact identification)

    3. Does it matter to them now? (Relevance determination)

    4. What do they need to know? (Filtered presentation).

3. Multi-Horizon Sensing (Where Information Comes From)

The system transforms the aircraft from a target being tracked by the ground into an intelligent diagnostic node. Awareness is managed across three horizons:

  • Horizon 1 — Aircraft Condition ("How am I?"): Embedded sensors monitor selected propulsion, structural, and mechanical parameters to detect developing anomalies or conditions that may have operational significance.

  • Horizon 2 — Local Airspace ("What's happening around me?"): Peer-to-peer mesh data allows aircraft to exchange relevant information directly with one another via the Proximity Awareness Network.

  • Horizon 3 — Wider System ("What's happening that could eventually affect me?"): The Integrated Short-Haul Operations Layer (ISHOL) provides broader context across Tower, TRACON, and En-Route domains.

4. Supporting Architecture (How Trust is Maintained)

Reliability is ensured through a three-layer governance framework designed to create a pathway toward subsequent certification evidence:

  • Data Governance (ISHOL): A federated fabric establishing common operational vocabulary without forcing a monolithic platform replacement.

  • Reasoning Governance (SA): A Bespoke AI Companion provides conversational guidance. To make AI-generated advisories auditable, every advisory follows a Diagnostic Provenance model: What source produced it? When was it valid? What transformation occurred? What rule bounded it?.

  • Action Governance (Runtime Assurance): A deterministic RTA layer acts as a symbolic gatekeeper. It monitors AI advisories and deterministically overrides any suggestion that violates a validated separation, procedural, or other hard safety constraint, reverting the system to manual control.

5. Distributed Computation (How Computation is Distributed)

To resolve the conflict between AI compute demands and aviation transparency, we adopt a three-tier model:

  • Tier 1 (Local Mandatory): Onboard environment for immediate sensing and the deterministic RTA.

  • Tier 2 (Distributed Mesh): Collaborative awareness and relevance propagation.

  • Tier 3 (Ground High-Performance): Large compute resources for wide-area prediction and trend analysis.

Mandate: Loss of distributed or ground computation may reduce capability, but must not compromise local aircraft safety.

6. Bounded Proof of Viability (How We Prove It)

The TRL 3 phase establishes experimental evidence of technical feasibility. The PoC is not intended to establish airworthiness; its purpose is to demonstrate technical and operational viability through five key demonstrations [LI 8]:

  1. Shared Operational Awareness: Constructing a useful common picture between nodes.

  2. Aircraft Intelligence: Onboard SA combining sensors and mesh data to identify developing conditions.

  3. Relevance Propagation: Identifying what changed, who is affected, and informing them without flooding the network.

  4. Distributed Aircraft Sensing: Onboard sensors detecting a simulated anomaly and propagating its operational significance to the appropriate participant without broadcasting irrelevant raw data.

  5. Graceful Degradation: Demonstrating that the system loses capability rather than safety when components (SA, mesh, or ground nodes) are removed.

7. Success Metrics & Personnel Impact (How We Measure Success)

  • Controller Cognitive Offloading Ratio (CCOR):

    • Target: Up to 65% reduction in measured routine cognitive burden, subject to experimental validation.

    • Mechanism: Shifting from "active data foraging" to "passive monitoring" by removing irrelevant noise while preserving human authority.

  • Operational Outcomes: Targeted reduction in voice-radio saturation and vectoring errors.

Conclusion: The System Defined

The HAH Retrofit Package is a trustworthy aviation operating environment where:

  • Aircraft sense themselves.

  • Aircraft sense their environment.

  • Aircraft talk to aircraft.

  • Aircraft and ground share relevant information.

  • The SA determines what matters to whom.

  • Humans make the decisions.

  • RTA prevents unsafe automated action.

  • The system degrades gracefully when components disappear.

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