Abstract
The envisioned transformation of the National Airspace System to integrate an In-time Aviation Safety Management System (IASMS) to assure safety in Advanced Air Mobility (AAM) brings unprecedented challenges to the design of human interfaces and management of safety information. Safety in design and operational safety assurance are critical factors for how humans will interact with increasingly autonomous systems. The IASMS Concept of Operations builds from traditional commercial operator safety management and scales in complexity to AAM. The transformative changes in future aviation systems pose potential new critical safety risks with novel types of aircraft and other vehicles having different performance capabilities, flying in increasingly complex airspace, and using adaptive contingencies to manage normal and non-normal operations. These changes compel development of new and emerging capabilities that enable innovative ways for humans to interact with data and manage information. Increasing complexity of AAM corresponds with use of predictive modeling, data analytics, machine learning, and artificial intelligence to effectively address known hazards and emergent risks. The roles of humans will dynamically evolve in increments with this technological and operational evolution. The interfaces for how humans will interact with increasingly complex and assured systems designed to operate autonomously and how information will need to be presented are important challenges to be resolved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
MITRE Corporation: The Future of Aerospace: Interconnected from Surface to Space. FAA Managers Association Managing the Skies 18(1), 12–15 (2020)
Verma, S., ET AL.: Lessons learned: using UTM paradigm for urban air mobility. In: 39th Digital Avionics Systems Conference (DASC), Virtual Event (2020)
Federal Aviation Administration: Unmanned Aircraft Systems (UAS) Traffic Management (UTM) Concept of Operations v2.0. FAA, Washington, DC (2020)
Patterson, M., et al.: An initial concept for intermediate-state, passenger-carrying urban air mobility operations. In: AIAA Sci Tech (2021)
National Academies of Sciences, Engineering, and Medicine: In-time Aviation Safety Management: Challenges and Research for an Evolving Aviation System. The National Academies Press, Washington, DC (2018). https://doi.org/10.17226/24962
National Academies of Sciences, Engineering, and Medicine: Advancing Aerial Mobility: A National Blueprint. The National Academies Press, Washington, DC (2020). https://doi.org/10.17226/25646
U.S. Department of Transportation Office of Inspector General: Weaknesses in FAA’s Certification and Delegation Processes Hindered Its Oversight of the 737 MAX 8. Report No. AV2021020. Washington, DC (2021)
Ellis, K., Krois, P., Koelling, J., Prinzel, L., Davies, M., Mah, R.: A concept of operations and design considerations for an in-time aviation safety management system (IASMS) for advanced air mobility (AAM). In: AIAA Sci Tech (2021)
Ellis, K., et al.: Defining services, functions, and capabilities for an advanced air mobility (AAM) in-time aviation safety management system (IASMS). In: AIAA Aviation (2021)
Daeschler, R.: The need for Safety Intelligence based on European safety data analysis. In: OPTICS Workshop (2015). http://www.optics-project.eu/optics1/wp-content/uploads/2015/04/04_R-Daeschler_The-need-for-Safety-Intelligence-based-on-European-safety-data-analysis_OPTICS-2nd-Workshop.pdf
Ellis, K., et al.: An approach for identifying IASMS services, functions, and capabilities. In: IEEE Digital Avionics Systems Conference (2021)
Bradford, S.: Foundation – global air traffic management concept of operations, ICAO document 9854, panel on operational sustainability in an increasingly congested and heterogeneous airspace. In: SCI TECH Forum, Virtual Event: AIAA (2022)
Lachter, J., Hobbs, A., Holbrook, J.: Thinking outside the box: the human role in increasingly automated aviation systems. In: International Symposium on Aviation Psychology (2021). https://ntrs.nasa.gov/citations/20210011929
Young, S., et al.: Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations. National Aeronautics and Space Administration Langley Research Center, Hampton, VA (2020). NASA/TM-2020–220440. https://ntrs.nasa.gov/citations/20200001140
American National Standards Institute: Standardization Roadmap for Unmanned Aircraft Systems, Version 1.0 (2018)
International Civil Aviation Organization: Safety Management, Standards and Recommended Practices–Annex 19. In: Convention on International Civil Aviation, 2nd Edition. ICAO: Montreal (2016)
Flight Deck Automation Working Group: Operational Use of Flight Path Management Systems. Performance-based operations Aviation Rulemaking Committee/ Commercial Aviation Safety Team (2013). https://www.faa.gov/aircraft/air_cert/design_approvals/human_factors/media/oufpms_report.pdf
Federal Aviation Administration: Summary of the FAA’s Review of the Boeing 737 MAX. FAA, Washington, DC (2020). https://www.faa.gov/foia/electronic_reading_room/boeing_reading_room/media/737_RTS_Summary.pdf
National Academies of Sciences, Engineering, and Medicine: Human-AI Teaming: State of the Art and Research Needs. The National Academies Press, Washington, DC (2021). https://nap.edu/26355
Wojton, H., Sparrow, D., Vickers, B., Carter, K., Wilkins, J., Fealing, C.: DAAWorks2021: characterizing human-machine teaming metrics for test and evaluation. institute for defense analyses (2021). https://www.ida.org/-/media/feature/publications/d/da/dataworks-2021-characterizing-human-machine-teaming-metrics-for-test-and-evaluation/d-21564.ashx
Das S., Matthews, B., Srivastava, A., Oza, N.: Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study. In: Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010), pp. 47–56 (2010). https://doi.org/10.1145/1835804.183513
Melnyk, I., Banerjee, A., Matthews, B., Oza, N.: Semi-Markov switching vector autoregressive model-based anomaly detection in aviation systems. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016)
Memarzadeh, M., Matthews, B., Avrekh, I.: Unsupervised anomaly detection in flight data using convolutional variational auto-encoder. Aerospace 7, 115. https://doi.org/10.3390/aerospace7080115
Federal Aviation Administration: Avionics Human Factors Considerations for Design and Evaluation. AC No: 00–74. FAA, Washington, DC (2019). https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_00-74.pdf
Brandt, S., Lachter, J.B., Russel, R., Shively, R.J.: A human-autonomy teaming approach for a flight-following task. In: Baldwin, C. (ed.) Advances in Neuroergonomics and Cognitive Engineering. AHFE Advances in Intelligent Systems and Computing, vol. 586, pp. 12–22. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60642-2_2
Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybernet. - Part A: Syst. Hum. 30(3), 286–297 (2000)
Holbrook, J.B., et al.: Enabling urban air mobility: human-autonomy teaming research challenges and recommendations. In: AIAA AVIATION Forum, Virtual Event: AIAA (2020)
Acknowledgement
The authors extend their appreciation to Ms. Laura Bass for her contributions in the development of this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply
About this paper
Cite this paper
Prinzel, L.J. et al. (2022). Human Interfaces and Management of Information (HIMI) Challenges for “In-Time” Aviation Safety Management Systems (IASMS). In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information: Applications in Complex Technological Environments. HCII 2022. Lecture Notes in Computer Science, vol 13306. Springer, Cham. https://doi.org/10.1007/978-3-031-06509-5_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-06509-5_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06508-8
Online ISBN: 978-3-031-06509-5
eBook Packages: Computer ScienceComputer Science (R0)