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Qualitative and quantitative risk assessment of urban airspace operations
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering. (AEAR)
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering. (AEAR)
ENAC, Toulouse, France.
Universidad Politécnica de Madrid, Madrid, Spain.
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2021 (English)In: SESAR Innovation Days, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Specific Operations Risk Assessment (SORA) is a qualitative methodology for assessing risks of drone operations. In this paper, SORA is compared to and complemented with quantitative estimations of the risk (earlier called HFRM: High-fidelity risk modeling). We highlight intrinsic shortcomings of both SORA and HFRM, and show how HFRM may help to deal with SORA’s ambiguities. (We do not have a recipe to remedy HFRM’s drawbacks with the help of SORA, but suggest a possible regulatory fix to HFRM, addressing its deficiency.) With its focus on ground risk, this paper complements the works of TU Dresden which suggested integrating “agent simulation as air risk assessment in SORA” [Fricke et al., ATM Seminar 2021] and of SESAR’s ER4 BUBBLES project “proposing a quantitative risk analysis which enhances or replaces the qualitative model of SORA” (also for the air risk) [BUBBLES Deliverable 4.1]; we also connect to CORUS observations on SORA shortcomings and use U-space services for addressing them. Our work advocates for stricter regulations, including digitalization and automation not only in definitions, but also in mandates/requirements. Our arguments are illustrated on simple synthetic cases and on real-world experimental examples from urban areas.

Place, publisher, year, edition, pages
2021.
Keywords [en]
Unmanned Aerial Systems, High-fidelity risk modeling, Specific Operations Risk Assessment, Ground risk, Air risk
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-187951OAI: oai:DiVA.org:liu-187951DiVA, id: diva2:1692058
Conference
SESAR Innovation Days (SID 2021), 7-9 of December, 2021
Available from: 2022-08-31 Created: 2022-08-31 Last updated: 2022-09-08Bibliographically approved

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Sedov, LeonidPolishchuk, Valentin

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
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  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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