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

说明与用途

采用零信任治理的神经符号规划器为城市空中移动提供可证明安全的路径规划，结合基于Transformer的轨迹建议、SMT验证和持续审计。

问题

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

结果

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

工作流

• 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.

证据

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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