SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities
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Abstract (arXiv)
The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given the unpredictable operational and environmental factors experienced by UAVs, which are hard to capture solely at design-time. This paper proposes a new reliability modeling approach called SafeDrones to help address this issue by enabling runtime reliability and risk assessment of UAVs. It is a prototype instantiation of the Executable Digital Dependable Identity (EDDI) concept, which aims to create a model-based solution for real-time, data-driven dependability assurance for multi-robot systems. By providing real-time reliability estimates, SafeDrones allows UAVs to update their missions accordingly in an adaptive manner.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2022.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2023Can Large Language Models assist in Hazard Analysis?cited by 8
- 2025Q-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metricscited by 1
- 2025Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeMLcited by 0
- 2026RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Controlcited by 0
- 2026RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control20 Jul 2026
- 2025Q-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metrics4 Sep 2025
- 2025Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML28 Aug 2025
- 2023Can Large Language Models assist in Hazard Analysis?25 Mar 2023