mlsanityData to 7 Oct 2026

A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems

arXiv 2205.04590 ↗First posted 9 May 2022eess.SY cs.AI cs.LGcited by 3

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Abstract (arXiv)

We present a safety verification framework for design-time and run-time assurance of learning-based components in aviation systems. Our proposed framework integrates two novel methodologies. From the design-time assurance perspective, we propose offline mixed-fidelity verification tools that incorporate knowledge from different levels of granularity in simulated environments. From the run-time assurance perspective, we propose reachability- and statistics-based online monitoring and safety guards for a learning-based decision-making model to complement the offline verification methods. This framework is designed to be loosely coupled among modules, allowing the individual modules to be developed using independent methodologies and techniques, under varying circumstances and with different tool access. The proposed framework offers feasible solutions for meeting system safety requirements at different stages throughout the system development and deployment cycle, enabling the continuous learning and assessment of the system product.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 41 references that are in the map. First posted on arXiv 9 May 2022.

Builds on

17 of 41 references.

References · 17

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2020Reinforcement Learning with Augmented Datacited by 452
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5 more
  1. 2017Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemblecited by 23
  2. 2019Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Drivingcited by 21
  3. 2018Falsification of Cyber-Physical Systems Using Deep Reinforcement Learningcited by 17
  4. 2021Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurancecited by 8
  5. 2017An Active Learning Approach to the Falsification of Black Box Cyber-Physical Systemscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2022A Survey on Reinforcement Learning in Aviation Applicationscited by 19
  2. 2022Falsification of Learning-Based Controllers through Multi-Fidelity Bayesian Optimizationcited by 4
  3. 2026Can We Formally Verify Neural PDE Surrogates? SMT Compilation of Small Fourier Neural Operatorscited by 0