Skip to main content
Springer Nature Link
Log in

Human-AI Teaming in the Urban Air Mobility Coordinator Work Position: A Proof-of-Concept Design

  • Conference paper
  • First Online:
Engineering Psychology and Cognitive Ergonomics (HCII 2025)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15777))

Included in the following conference series:

  • 309 Accesses

Abstract

Drones and vertical take-off and landing aircraft are set to revolutionise mobility in cities, necessitating advanced traffic management solutions for the low-level U-space airspace. The transition from piloted to autonomous operations will require a shift from traditional air traffic management to highly automated systems, while human oversight remains critical for handling emergencies and operational uncertainties. This paper presents a proof-of-concept design for a Urban Air Mobility (UAM) Coordinator work position, where a human operator collaborates with an AI-based Digital Assistant (DUC) to manage futuristic U-space operations in Stockholm, Sweden. The study explores the feasibility, challenges, and opportunities of U-space traffic management while examining effective human-AI teaming. The proposed UAM Control Centre, operating under a U-space Service Provider, integrates 25 U-space services, balancing automation with human oversight. The design of the workstation includes a three-screen interface for situational awareness, communication, and decision support. The DUC improves efficiency by managing routine tasks like conformance monitoring, while the UAM Coordinator focusses on strategic decision making. Developed through a two-year co-design process with air traffic management and U-space experts, this concept aligns with European U-space regulations. Future simulations will further evaluate the performance of the DUC in improving safety and efficiency. The study advances the development of human-centric Human AI-Teaming (HAT) solutions to enhance safety and efficiency in safety-critical environments, with a particular focus on complex urban airspaces.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
€34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 53.49
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 70.61
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

') var buybox = document.querySelector("[data-id=id_"+ timestamp +"]").parentNode var buyingOptions = buybox.querySelectorAll(".buying-option") ;[].slice.call(buyingOptions).forEach(initCollapsibles) var buyboxMaxSingleColumnWidth = 480 function initCollapsibles(subscription, index) { var toggle = subscription.querySelector(".buying-option-price") subscription.classList.remove("expanded") var form = subscription.querySelector(".buying-option-form") var priceInfo = subscription.querySelector(".price-info") var buyingOption = toggle.parentElement if (toggle && form && priceInfo) { toggle.setAttribute("role", "button") toggle.setAttribute("tabindex", "0") toggle.addEventListener("click", function (event) { var expandedBuyingOptions = buybox.querySelectorAll(".buying-option.expanded") var buyboxWidth = buybox.offsetWidth ;[].slice.call(expandedBuyingOptions).forEach(function(option) { if (buyboxWidth buyboxMaxSingleColumnWidth) { toggle.click() } else { if (index === 0) { toggle.click() } else { toggle.setAttribute("aria-expanded", "false") form.hidden = "hidden" priceInfo.hidden = "hidden" } } }) } initialStateOpen() if (window.buyboxInitialised) return window.buyboxInitialised = true initKeyControls() })()

Institutional subscriptions

Similar content being viewed by others

References

  1. 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

  2. EUROCONTROL.: U-space ConOps and architecture (edition 4). CORUS-XUAM Technical report (2023). https://www.sesarju.eu/node/4544. Accessed 20 Apr 2024

  3. Airservices Australia and Embraer Business Innovation Center: Urban Air Traffic Management. Concept of Operations. Version 1 (2020). https://engage.airservicesaustralia.com/urban-air-traffic-management-concept-of-operations. Accessed 20 Apr 2024

  4. EASA: Study on the societal acceptance of Urban Air Mobility in Europe (2021). https://www.easa.europa.eu/sites/default/files/dfu/uam-full-report.pdf. Accessed 10 Jan 2025

  5. EVE Air Mobility: Urban air mobility concept of operations for the London environment (2022). https://www.eveairmobility.com/uk-consortium-completes-urban-air-mobility-concept-of-operations-for-the-civil-aviation-authority/. Accessed 12 June 2024

  6. Lyons, J.B., Sycara, K., Lewis, M., Capiola, A.: Human-autonomy teaming: definitions, debates, and directions. Front. Psychol. 12(589585) (2021). https://doi.org/10.3389/fpsyg.2021.589585

  7. National Academies of Sciences, Engineering, and Medicine: Human-AI Teaming: State-of-the-Art and Research Needs. The National Academies Press, Washington, DC (2022). https://doi.org/10.17226/26355

  8. Endsley, M.R.: Supporting human-AI teams: transparency, explainability, and situation awareness. Comput. Hum. Behav. 140, 107574 (2023). https://doi.org/10.1016/j.chb.2022.107574

  9. O’Neill, T., McNeese, N., Barron, A., Schelble, B.: Human-autonomy teaming: a review and analysis of the empirical literature. Hum. Factors 64(5), 904–938 (2022). https://doi.org/10.1177/0018720820960865

    Article  Google Scholar 

  10. EASA: Artificial Intelligence Roadmap 2.0. Human-centric approach to AI in aviation (2020). https://www.easa.europa.eu/en/document-library/general-publications/easa-artificial-intelligence-roadmap-20. Accessed 10 Jan 2025

  11. Westin, C., Borst, C., Hilburn, B.: Automation transparency and personalized decision support: air traffic controller interaction with a resolution advisory system. IFAC-PapersOnLine 49(19), 201–206 (2016). https://doi.org/10.1016/j.ifacol.2016.10.520

    Article  Google Scholar 

  12. Chen, J.Y.C., Barnes, M.J.: Human-agent teaming for multirobot control: a review of human factors issues. IEEE Trans. Hum.-Mach. Syst. 44(1), 13–29 (2014). https://doi.org/10.1109/THMS.2013.2293535

    Article  Google Scholar 

  13. Schaefer, K.E., Straub, E.R., Chen, J., Putney, J., Evans, A.W.: Communicating intent to develop shared situation awareness and engender trust in human-agent teams. Cogn. Syst. Res. 46, 26–39 (2017). https://doi.org/10.1016/j.cogsys.2017.02.002

    Article  Google Scholar 

  14. Battiste, V., Lachter, J., Brandt, S., Alvarez, A., Strybel, T.Z., Vu, K.-P.L.: Human-automation teaming: lessons learned and future directions. In: Yamamoto, S., Mori, H. (eds.) HIMI 2018. LNCS, vol. 10905, pp. 479–493. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92046-7_40

    Chapter  Google Scholar 

  15. McNeese, N.J., Demir, M., Cooke, N.J., Myers, C.: Teaming with a synthetic teammate: insights into human-autonomy teaming. Hum. Factors 60(2), 262–273 (2018). https://doi.org/10.1177/0018720817743223

    Article  Google Scholar 

  16. Westin, C., Boonsong, S., Josefsson B., Lundberg, J.: Building trust in autonomous system competence - the DiTA digital tower assistant for multiple remote towers, an early concept evaluation. In: 39th AIAA/IEEE DASC, San Antonio, TX, USA, pp. 1–9 (2020). https://doi.org/10.1109/DASC50938.2020.9256759

  17. Lundin Palmerius, K., Henriksson, M., Westin, C., Lundberg, J.: Digital tower assistant functionality and design: planning, analysis and operative interfaces based on workshops with ATCOs. In: 12th SID, Budapest, Hungary (2022)

    Google Scholar 

  18. Jameel, M., Tyburzy, L., Gerdes, I., Pick, A., Hunger, R., Christoffels, L.: Enabling digital air traffic controller assistant through human-autonomy teaming design. In: 42nd IEEE/AIAA DASC, Barcelona, Spain, pp. 1–9 (2023). https://doi.org/10.1109/DASC58513.2023.10311220

  19. Lundberg, J., Johansson, B.: A framework for describing interaction between human operators and autonomous, automated, and manual control systems. Cogn. Technol. Work 23, 381–401 (2021). https://doi.org/10.1007/s10111-020-00637-w

    Article  Google Scholar 

  20. Nylin, M., Westberg, J.J., Lundberg, J.: Reduced autonomy workspace (raw)-an interaction design approach for human-automation cooperation. Cogn. Technol. Work. 24(2), 261–273 (2022). https://doi.org/10.1007/s10111-022-00695-2

    Article  Google Scholar 

  21. Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors 37(1), 32–64 (1995). https://doi.org/10.1518/001872095779049543

    Article  Google Scholar 

  22. EVE Air Mobility: Concept of Operations for sustainable urban air mobility in Rio De Janeiro. Brazil (2022). https://eveairmobility.com/wp-content/uploads/2022/05/EveConopsRJ.pdf. Accessed 12 June 2024

  23. FAA: Unmanned Aircraft System (UAS) Traffic Management (UTM) Concept of Operations v2.0. Federal Aviation Administration, Washington (2020)

    Google Scholar 

  24. Boeing: Concept of Operations for Uncrewed Urban Air Mobility (2022). https://www.boeing.com/content/dam/boeing/boeingdotcom/innovation/con-ops/docs/Concept-of-Operations-for-Uncrewed-Urban-Air-Mobility.pdf. Accessed 19 Apr 2024

  25. FOCA: Swiss U-Space ConOps. Swiss Federal Office of Civil Aviation (FOCA) (2020)

    Google Scholar 

  26. Goodrich, K.H., Theodore, C.R.: Description of the NASA urban air mobility maturity level (UML) scale. In: AIAA Scitech Forum. Virtual Event (2021). https://doi.org/10.2514/6.2021-1627

  27. Levitt, I., Phojanamongkolkij, N., Horn, A., Witzberger, K.: UAM airspace research roadmap rev 2.0. NASA/TM-20230002647. NASA Langley Research Center, Hampton (2023)

    Google Scholar 

  28. EASA: EASA Artificial Intelligence (AI) Concept Paper Issue 2: Guidance for Level 1 &2 machine learning applications (2024). https://www.easa.europa.eu/en/document-library/general-publications/easa-artificial-intelligence-concept-paper-issue-2. Accessed 10 Jan 2025

  29. Westberg, J.J., Palmerius, K.L., Lundberg, J.: UTM city-visualization of unmanned aerial vehicles. IEEE Comput. Graph. Appl. 42(5), 84–89 (2022). https://doi.org/10.1109/MCG.2022.3193160

    Article  Google Scholar 

  30. Nylin, M., Lundberg, J., Bång, M., Kucher, K.: Glyph design for communication initiation in real-time human-automation collaboration. Vis. Inform. 8(4), 23–35 (2024). https://doi.org/10.1016/j.visinf.2024.09.006

    Article  Google Scholar 

  31. Simon, H.A.: The structure of ill-structured problems. Artif. Intell. 4(3), 181–201 (1973). https://doi.org/10.1016/0004-3702(73)90011-8

    Article  Google Scholar 

Download references

Acknowledgments

This publication is based on work performed in the HAIKU Project which has received funding from the European Union’s Horizon Europe research and innovation programme, under Grant Agreement no 101075332. Any dissemination reflects the authors’ views only, and the European Commission is not responsible for any use that may be made of information it contains.

Author information

Authors and Affiliations

  1. Linköping University, 581 83, Linköping, Sweden

    Carl Westin, Karl Johan Klang, Jekaterina Basjuka, Gustaf Söderholm, Jonas Lundberg, Magnus Bång & Karljohan Lundin Palmerius

  2. LFV, 601 79, Norrköping, Sweden

    Supathida Boonsong, Åsa Taraldsson & Gustaf Fylkner

Authors
  1. Carl Westin
    View author publications

    Search author on:PubMed Google Scholar

  2. Karl Johan Klang
    View author publications

    Search author on:PubMed Google Scholar

  3. Jekaterina Basjuka
    View author publications

    Search author on:PubMed Google Scholar

  4. Gustaf Söderholm
    View author publications

    Search author on:PubMed Google Scholar

  5. Jonas Lundberg
    View author publications

    Search author on:PubMed Google Scholar

  6. Magnus Bång
    View author publications

    Search author on:PubMed Google Scholar

  7. Karljohan Lundin Palmerius
    View author publications

    Search author on:PubMed Google Scholar

  8. Supathida Boonsong
    View author publications

    Search author on:PubMed Google Scholar

  9. Åsa Taraldsson
    View author publications

    Search author on:PubMed Google Scholar

  10. Gustaf Fylkner
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Carl Westin .

Editor information

Editors and Affiliations

  1. Coventry University, Coventry, UK

    Don Harris

  2. Cranfield University, Cranfield, UK

    Wen-Chin Li

Ethics declarations

Disclosure of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

A Appendix

A Appendix

1.1 A.1 Glossary

Terminology Used in this Article

AAM = Advanced Air Mobility. Refers to innovative and disruptive airborne technology to transport people and goods to areas beyond the reach of traditional air transport, including complex and rural urban environments [1].

Air Taxi = aircraft with or without pilot on board that carries passengers [2].

CIS = The Common Information Service distributes data to support the provision of U-space services [2].

CORUS-XUAM = European U-space concept of operations developed as part of a European Horizon 2020 funded project [2].

Drone = aircraft without an on-board pilot, also known as UAS [2].

Emergency Responders = organisations that handle emergency operations, including fire brigades, medical services, and Search and Rescue teams [2].

Geo-fence zone = a defined airspace volume with operational requirements or constraints that can restrict access to aircraft. If no operations are allowed, it is referred to as a no-fly zone.

UAM = Urban Air Mobility. Refers to aircraft-based means of transportation near or within cities [2].

UAM Operations = air operations above urban areas, in U-space airspace, carried out by a mix of aircraft with limited range and unable to fly visual or instrument flight rules, which require tactical separation [2].

UAS = Uncrewed/Unmanned Aerial System. Aircraft that can carry passengers but is usually piloted remotely or autonomously [2].

UTM = UAS Traffic Management. An ecosystem for traffic management of UAS operations [2].

U-space airspace = the airspace that contains the UAS and UAM operations [2].

U-space services = European system to manage UAS and UAM operations [2].

U-plan = flight plan in U-space airspace [2].

UAM Control Centre (UCC) = the office of the UAM Coordinator and the DUC.

Vertiport = similar to conventional airports, these are dedicated ground-based facilities that support the take-off and landing of aircraft, including UAS and piloted aircraft such as VTOL and helicopters.

VTOL = Aircraft capable of Vertical Take-OFF and Landing [2].

Roles Referred to in this Article

DUC = Digital Assistant for the UAM Coordinator. A conceptual AI-based intelligent assistant that collaborates with the UAM Coordinator to manage the U-space and provide U-space services.

UAM Coordinator = human actor responsible for the tactical management of the U-space and the provision of U-space services.

UAM Operator = legal entity that operates and is responsible for one or more UAM flights that carry passengers or goods [2].

UAS Operator = legal entity that operates and is responsible for one or more UAS flights [2].

U-space Service Provider (USSP) = stakeholder providing U-space services [2].

Vertiport Operator = entity that manages and provides vertiport services, including accommodating incoming aircraft [2].

1.2 A.2 DUC HAT Requirements

Situation Awareness

  • DUC should be able to continuously monitor the U-space and traffic operations, providing real-time updates and alerts to the UAM Coordinator.

  • DUC should be able to monitor the U-space by collecting real-time data from multiple sources, including data from aircraft and weather.

  • DUC should be able to process the incoming data to identify trends and detect anomalies.

  • DUC should be able to generate status reports on U-space operations, incidents, and performance metrics.

  • DUC should be able to detect potential conflicts between UAS/UAM vehicles, such as near-collisions or airspace violations.

  • DUC should be able to perform simulations and scenario planning to anticipate future traffic patterns and potential U-space capacity issues.

  • DUC should be able to retrieve and present information on the request of the UAM Coordinator.

  • DUC should be able to infer what information the UAM Coordinator needs and present it.

  • DUC should be able to call for/direct the UAM Coordinator’s attention to important information (e.g., attention guidance).

Transparency

  • DUC should be able to provide explanations on request.

  • DUC should be able to correctly determine and understand what the UAM Coordinator is trying to understand, for which an explanation is needed.

  • DUC should be able to demonstrate the relevance of an explanation for a decision/action.

  • DUC should be able to determine an appropriate level of abstraction of an explanation according to the task, situation, trust, and expertise of the UAM Coordinator.

  • DUC should be able to explain how it derived an output.

  • DUC should be able to explain how it works.

Bidirectional Communication

  • DUC should be able to provide indication of having acknowledged the UAM Coordinators’ instructions/intentions.

  • DUC should be able to understand and generate human natural language.

  • DUC should be able to communicate using different modalities, including voice, text, and graphics (e.g., highlight areas on the map).

  • DUC should be able to not interfere when the UAM Coordinator is involved in other communications or actions.

  • DUC should be able to automatically adapt the modality of interactions to end-user states, preferences, and situations

Decision Making

  • DUC should be able to recommend actions/solutions.

  • DUC should be able to decide and implement actions within its performance envelope.

  • The DUC should allow the UAM Coordinator to adjust some of the authority limits and constraints in decision making and action implementation.

  • DUC should be able to identify poor and suboptimal strategies/actions/solutions proposed by the UAM Coordinator.

  • DUC should be able to propose and justify optimised solutions, where applicable.

  • DUC should be able to propose alternative strategies/actions/solutions.

  • DUC should be able to solve problems with the UAM Coordinator following a checklist.

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Westin, C. et al. (2025). Human-AI Teaming in the Urban Air Mobility Coordinator Work Position: A Proof-of-Concept Design. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. HCII 2025. Lecture Notes in Computer Science(), vol 15777. Springer, Cham. https://doi.org/10.1007/978-3-031-93721-7_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-93721-7_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-93720-0

  • Online ISBN: 978-3-031-93721-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics