# SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities

arXiv: https://arxiv.org/abs/2207.05643 · HTML: https://mlsanity.com/paper/2207.05643/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2022.

## Built on by (most cited first, 4 of 4)

- [Can Large Language Models assist in Hazard Analysis?](https://mlsanity.com/paper/2303.15473/) (2023)
- [Q-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metrics](https://mlsanity.com/paper/2509.04536/) (2025)
- [Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML](https://mlsanity.com/paper/2508.20776/) (2025)
- [RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control](https://mlsanity.com/paper/2607.17951/) (2026)

## 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.