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

• URL: http://arxiv.org/abs/2205.04590v1
• Date: Mon, 9 May 2022 22:56:00 GMT
• Title: A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems
• Authors: Ali Baheri, Hao Ren, Benjamin Johnson, Pouria Razzaghi, Peng Wei
• Abstract summary: We present a safety verification framework for design-time and run-time assurance of learning-based components in aviation systems. 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.
• Score: 6.168537302126847
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• ScenicProver: A Framework for Compositional Probabilistic Verification of Learning-Enabled Systems [3.4880795442123733]
  This paper introduces ScenicProver, a verification framework for learning-enabled cyber-physical systems.<n>It supports compositional system description with clear component interfaces, ranging from interpretable code to black boxes.<n>We demonstrate the framework's effectiveness through a case study on an autonomous vehicle's emergency braking system with sensor fusion.
  arXiv  Detail & Related papers  (2025-11-04T01:09:08Z)
• Vision: An Extensible Methodology for Formal Software Verification in Microservice Systems [0.0]
  Microservice systems are becoming increasingly adopted due to their scalability, decentralized development, and support for continuous integration and delivery.<n>We propose a novel methodology that statically reconstructs microservice source code into a formal system model.
  arXiv  Detail & Related papers  (2025-09-02T22:11:46Z)
• BlueGlass: A Framework for Composite AI Safety [0.2999888908665658]
  This paper introduces BlueGlass, a framework designed to facilitate AI safety by providing a unified infrastructure.<n>To demonstrate the utility of this framework, we present three safety-oriented analyses on vision-language evaluation.<n>More broadly, this work contributes infrastructure and findings for building more robust and reliable AI systems.
  arXiv  Detail & Related papers  (2025-07-14T09:45:34Z)
• Learning Verifiable Control Policies Using Relaxed Verification [49.81690518952909]
  This work proposes to perform verification throughout training to aim for policies whose properties can be evaluated throughout runtime.<n>The approach is to use differentiable reachability analysis and incorporate new components into the loss function.
  arXiv  Detail & Related papers  (2025-04-23T16:54:35Z)
• SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach [58.93030774141753]
  Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence. This paper conceptualizes cybersafety and cybersecurity in the context of multimodal learning. We present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats.
  arXiv  Detail & Related papers  (2024-11-17T23:06:20Z)
• Semi-Supervised Multi-Task Learning Based Framework for Power System Security Assessment [0.0]
  This paper develops a novel machine learning-based framework using Semi-Supervised Multi-Task Learning (SS-MTL) for power system dynamic security assessment. The learning algorithm underlying the proposed framework integrates conditional masked encoders and employs multi-task learning for classification-aware feature representation. Various experiments on the IEEE 68-bus system were conducted to validate the proposed method.
  arXiv  Detail & Related papers  (2024-07-11T22:42:53Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• A Model Based Framework for Testing Safety and Security in Operational Technology Environments [0.46040036610482665]
  We propose a model-based testing approach which we consider a promising way to analyze the safety and security behavior of a system under test. The structure of the underlying framework is divided into four parts, according to the critical factors in testing of operational technology environments.
  arXiv  Detail & Related papers  (2023-06-22T05:37:09Z)
• In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States [84.24300005271185]
  We propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.
  arXiv  Detail & Related papers  (2023-01-27T22:28:19Z)
• A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems [128.63953314853327]
  "Lifelong Learning" systems are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. We show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems.
  arXiv  Detail & Related papers  (2023-01-18T21:58:54Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• Reliability Assessment and Safety Arguments for Machine Learning Components in Assuring Learning-Enabled Autonomous Systems [19.65793237440738]
  We present an overall assurance framework for Learning-Enabled Systems (LES) We then introduce a novel model-agnostic Reliability Assessment Model (RAM) for ML classifiers. We discuss the model assumptions and the inherent challenges of assessing ML reliability uncovered by our RAM.
  arXiv  Detail & Related papers  (2021-11-30T14:39:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.