首页|Safety Monitoring of Machine Learning Perception Functions: A Survey

Safety Monitoring of Machine Learning Perception Functions: A Survey

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  • NETL
  • NSTL
  • Wiley
Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks.Newdependability challenges arise whenMLpredictions are used in safety-critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms, such as safety monitors, is essential to ensure the safe behavior of the system despite the occurrence of faults. This paper presents an extensive literature review on safety monitoring of perception functions using ML in a safety-critical context. In this review, we structure the existing literature to highlight key factors to consider when designing such monitors: threat identification, requirements elicitation, detection of failure, reaction, and evaluation.We also highlight the ongoing challenges associated with safety monitoring and suggest directions for future research.

fault tolerancemachine learning perceptionruntime monitoringsafety-critical autonomous systems

Raul Sena Ferreira、Joris Guerin、Kevin Delmas、Jeremie Guiochet、HeleneWaeselynck

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LAAS/CNRS, Toulouse, France||Universite de Toulouse, Toulouse, France

LAAS/CNRS, Toulouse, France||Universite de Toulouse, Toulouse, France||Espace-Dev, IRD, Universite de Montpellier, Montpellier, France

ONERA, Toulouse, France

LAAS/CNRS, Toulouse, France

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2025

10.1111/coin.70032

Computational Intelligence

Computational Intelligence

ISSN：0824-7935
年,卷(期)：2025.41(2)
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