mlsanityData to 14 Oct 2026

Certifying Emergency Landing for Safe Urban UAV

arXiv 2104.14928 ↗First posted 30 Apr 2021cs.RO cs.CVcited by 6

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Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments. However, allowing UAVs to fly above densely populated areas raises concerns regarding safety. One of the main safety issues is the possibility for a failure to cause the loss of navigation capabilities, which can result in the UAV falling/landing in hazardous areas such as busy roads, where it can cause fatal accidents. Current standards, such as the SORA published in 2019, do not consider applicable mitigation techniques to handle this kind of hazardous situations. Consequently, certifying UAV urban operations implies to demonstrate very high levels of integrity, which results in prohibitive development costs. To address this issue, this paper explores the concept of Emergency Landing (EL). A safety analysis is conducted on an urban UAV case study, and requirements are proposed to enable the integration of EL as an acceptable mitigation mean in the SORA. Based on these requirements, an EL implementation was developed, together with a runtime monitoring architecture to enhance confidence in the system. Preliminary qualitative results are presented and the monitor seem to be able to detect errors of the EL system effectively.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 5 of its 33 references that are in the map. First posted on arXiv 30 Apr 2021.

Builds on

5 of 33 references.

References · 5

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2018Evaluating Bayesian Deep Learning Methods for Semantic Segmentationcited by 143
  3. 2020Bayesian Neural Networks: An Introduction and Surveycited by 131
  4. 2019Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescuecited by 11
  5. 2020Robust Autonomous Landing of UAV in Non-Cooperative Environments based on Dynamic Time Camera-LiDAR Fusioncited by 3

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 1; 2025: 2.

6 papers.