Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning

• URL: http://arxiv.org/abs/2511.20811v1
• Date: Tue, 25 Nov 2025 19:57:07 GMT
• Title: Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning
• Authors: Aaron O. Feldman, D. Isaiah Harp, Joseph Duncan, Mac Schwager,
• Abstract summary: We develop a data-driven approach for safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters.<n>We use offline trajectory simulation to learn a calibrated statistical model of the short-term safety risk facing pilots.<n>We evaluate our method on a flight dynamics model with uncertain parameters, demonstrating its ability to reliably identify unsafe scenarios.
• Score: 7.0366838632617705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a data-driven approach for runtime safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters. Because safety violations can arise unexpectedly as a result of these uncertainties, pilots need clear, preemptive criteria to abort the maneuver in advance of safety violation. To solve this problem, we use offline stochastic trajectory simulation to learn a calibrated statistical model of the short-term safety risk facing pilots. We use flight testing as a motivating example for data-driven learning/monitoring of safety due to its inherent safety risk, uncertainty, and human-interaction. However, our approach consists of three broadly-applicable components: a model to predict future state from recent observations, a nearest neighbor model to classify the safety of the predicted state, and classifier calibration via conformal prediction. We evaluate our method on a flight dynamics model with uncertain parameters, demonstrating its ability to reliably identify unsafe scenarios, match theoretical guarantees, and outperform baseline approaches in preemptive classification of risk.

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