AI RESEARCH
Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing
arXiv CS.LG
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ArXi:2604.26411v1 Announce Type: new 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