Document Type
Article
Publication Date
5-31-2023
Abstract
Enabling new and novel concepts of operations for Advanced Air Mobility poses an important need to evolve current safety management systems (SMS) and is posited to be realized through advances in Machine Learning (ML) Data Sciences and Artificial Intelligence. The “In-time Aviation Safety Management System” (IASMS) concept of operations supports the need to evolve today’s SMS to become more tailorable, scalable, and interoperable in response to forecasted changes expected for the future airspace system. Key to IASMS is integration of proactive and predictive ML algorithms trained to provide “in time” detection and mitigation of hazards and emergent risks through new methods and novel data types. IASMS research and technology development includes human factors design considerations for these systems to include human-system teaming, innovations in human interfaces and management of complex digital data information, human-system interaction/model-based system engineering, and verification and validation for data assurance and trust.
Repository Citation
Stephens, C. L.,
Prinzel, L. J.,
Ellis, K. K.,
Vincent, M. J.,
Infeld, S. I.,
Oza, N. C.,
Davies, M. D.,
Mah, R. W.,
Krois, P. A.,
& Ackerson, J.
(2023). Research and Technology Challenges for Human Data Analysts in Future Safety Management Systems. 22nd International Symposium on Aviation Psychology, 32.
https://corescholar.libraries.wright.edu/isap_2023/27
