用数据驱动方法提前预警飞行测试中的安全隐患。
Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning
- 基于历史数据预测飞行状态，结合最近邻分类评估风险。
- 通过置信区间校准，实现安全风险的可靠预判与理论保证。
- 适合高风险场景下需实时决策的人机交互系统应用。
我们提出一种数据驱动的飞行测试运行时安全监控方法，针对飞行员在参数不确定的飞机上执行机动时可能突发的安全问题。为避免事故，需要提前提供明确的中止准则。为此，我们利用离线随机轨迹仿真，学习短期安全风险的校准统计模型。以飞行测试为例，因其固有的安全风险、不确定性与人机交互特性，成为数据驱动安全学习的理想场景。本方法包含三个通用模块：从近期观测预测未来状态的模型、用于判断预测状态安全性的最近邻分类器，以及通过合规定性预测进行分类器校准。在具有不确定参数的飞行动力学模型上评估表明，该方法能可靠识别不安全场景，满足理论保证，并优于基线方法在风险提前预警上的表现。
原文摘要 · Abstract (English)
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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