Demarcation method of safety separations for sUAV based on collision risk estimation

Gang Zhong, Sen Du, Honghai Zhang, Jiangying Zhou and Hao Liu

Reliability Engineering and System Safety, 2024, vol. 242, issue C

Abstract: Maintaining safe separations is essential to achieve reliable operations for unmanned aerial vehicles (UAV). Given the escalating severity of collision risks associated with UAVs, separation criteria have emerged as foundational to both airspace design and collision avoidance strategies. However, without adequate consideration of the characteristics of small-UAV (sUAV) encounters and airspace structure, most of the existing models focus on assessing uniform separation volume with manned aircraft. This paper proposes a demarcation method of safety separation for sUAVs based on collision risk estimation. Firstly, five collision zones are developed for collision determination by considering the shape characteristics. Then, the collision risk model is established from the 3-D probability density function (PDF) with the supremum of total system error (TSE). Finally, the minimum safe separations for different encountering scenarios are demarcated by setting the threshold of risk. Simulation results testify that the selection of the collision zones and crossing angles affect the collision risk apparently. The results also demonstrate the effectiveness of separation criteria demarcated by ‘Equivalent Level of Safety’ (ELoS). Statistical analysis for 100 sampled environments shows the reliability of the proposed method, which captured the characteristics of sUAVs and could be utilized for elaborative separation framework in structured low-altitude airspace.

Keywords: UAV separation criteria; Collision risk model; Equivalent level of safety; Sensitivity analysis; Low-altitude airspace design (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:242:y:2024:i:c:s095183202300652x

DOI: 10.1016/j.ress.2023.109738

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