统一三类运行时监控，保障飞行视觉着陆安全
Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing
- 将监控分为环境、分布、模型三类，系统化设计安全防护
- 在飞机着陆视觉检测中验证，显著提升异常识别能力
- 适合自动驾驶、无人机等高危场景的可靠性设计
运行时监控对保障安全关键领域中机器学习应用的安全性至关重要。然而，现有研究分散，不同社区独立发展出多种方法。本文提出一个统一框架，将运行时监控方法分为三类：运行设计域（ODD）监控，确保系统在预期工作条件下运行；分布外（OOD）监控，拒绝偏离训练数据的输入；模型范围外（OMS）监控，基于模型内部状态或输出检测异常行为。我们在航空安全关键应用——着陆阶段的跑道检测任务中，通过专门实验验证了该分类体系的优势。该框架有助于系统设计监控策略，支持多类监控器协同，并可使用统一的安全导向指标进行评估与比较。
原文摘要 · Abstract (English)
Runtime monitoring is essential to ensure the safety of ML applications in safety-critical domains. However, current research is fragmented, with independent methods emerging from different communities. In this paper, we propose a unified framework categorising runtime monitoring approaches into three distinct types: Operational Design Domain (ODD) monitoring, which ensures compliance with expected operating conditions; Out-of-Distribution (OOD) monitoring, which rejects inputs that deviate from the training data; and Out-of-Model-Scope (OMS) monitoring, which detects anomalous model behaviour based its internal states or outputs. We demonstrate the benefits of this categorization with a dedicated experiment on an aeronautical safety-critical application: runway detection during landing. This framework facilitates design of monitoring activities, with complementary categories of monitors, and enables evaluation and comparison of different monitors using common, safety-oriented metrics.
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