May 11, 2022, 1:11 a.m. | Ali Baheri, Hao Ren, Benjamin Johnson, Pouria Razzaghi, Peng Wei

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv aviation framework learning safety safety-critical systems verification

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