Adaptive Neuro‑Symbolic Planning for Autonomous Urban Air Mobility Routing with Zero‑Trust Governance Guarantees

Description and use

A neuro‑symbolic planner with zero‑trust governance guarantees provable safety for autonomous urban air mobility routing, combining transformer‑based trajectory proposals with SMT verification and continuous audit.

Problem

Neural planners lack provable safety guarantees while symbolic planners cannot handle dynamic, noisy environments and real‑time latency, making autonomous UAM routing difficult.

Outcome

The prototype achieved higher acceptance rates by incorporating verification‑aware loss and produced provably safe routes while respecting real‑time latency.

Workflow

• 1. Perception layer fuses sensor data and predicts traffic.
• 2. Neural proposal network (transformer‑based) generates multiple candidate trajectories.
• 3. Symbolic verification layer (SMT solver + temporal logic) filters candidates for constraint compliance.
• 4. Zero‑trust governance layer attests inputs, monitors runtime, and audits the pipeline.
• 5. Accepted trajectory is executed by the aircraft.

Evidence

Neural networks are brilliant at pattern recognition and approximate reasoning, but they're notoriously terrible at guaranteeing anything.

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Symbolic planners can prove properties about their outputs, but they crumble when the environment is noisy, dynamic, and full of uncertainty.

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The key insight is that each layer has a different trust model. The neural network is untrusted by default — it proposes, but never commits.

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Training the network with a verification‑aware loss dramatically improved the acceptance rate.

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