Web: http://arxiv.org/abs/2205.04590

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

More from arxiv.org / cs.LG updates on arXiv.org

Data Analyst, Patagonia Action Works

@ Patagonia | Remote

Data & Insights Strategy & Innovation General Manager

@ Chevron Services Company, a division of Chevron U.S.A Inc. | Houston, TX

Faculty members in Research areas such as Bayesian and Spatial Statistics; Data Privacy and Security; AI/ML; NLP; Image and Video Data Analysis

@ Ahmedabad University | Ahmedabad, India

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC