Real-time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM)

Ancel, Ersin; Capristan, Francisco M.; Foster, John V.; Condotta, Ryan C. · 2017 · OpenAlex-citations

DOI: 10.2514/6.2017-3273

archive: archived pipeline: cataloged verified

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Summary

This paper introduces the Unmanned Aircraft System Traffic Management Risk Assessment Framework (URAF), a methodology designed to provide real-time safety evaluation for small unmanned aircraft systems (sUAS) operating within the National Airspace System. The research is motivated by the need to support future commercial sUAS operations, such as beyond visual line-of-sight flights and package delivery, which require robust safety tracking over populated areas. While existing regulations restrict sUAS operations to visual line-of-sight and prohibit flights over people, NASA’s UTM concept aims to facilitate safe low-altitude airspace usage. The authors identify a gap in current literature, noting that while third-party risk models exist, none offer a dynamic, real-time assessment capability that integrates live aircraft health data. The URAF utilizes a modular architecture comprising three primary components: a Probabilistic Graphical Model, an Off-Nominal Trajectory and Impact Point Prediction module, and a Casualty Estimation module. The Probabilistic Graphical Model employs Bayesian Belief Networks (BBNs) to estimate the likelihood of mishaps by fusing real-time telemetry data—such as battery status, GPS health, and wind conditions—with historical failure data. This approach allows for the continuous updating of mishap probabilities based on observed evidence, addressing the lack of empirical sUAS operational data. The Trajectory module predicts the potential impact zone following an off-nominal event, such as an unpowered descent, using algorithms like the AirSTAR Impact Point Prediction method. Finally, the Casualty Estimation module calculates the expected number of casualties ($E_c$) by combining the predicted impact area with population density data. Risk severity is determined by weighting kinetic energy and casualty estimates, which are then mapped onto a modified FAA risk matrix to classify risk levels as low, medium, or high. To demonstrate the framework, the authors conducted a simulated case study using a DJI S1000 octocopter flying an autonomous trajectory over NASA Langley Research Center. The simulation integrated real-time telemetry via MAVLINK messages with Python and MATLAB scripts to execute the BBN model and trajectory predictions. The system visualized the risk on a ground control station, updating the likelihood of an unpowered descent and the associated casualty risk based on the aircraft's position relative to simulated populations. The study assumed static population density and low-fidelity aircraft models to validate the feasibility of linking aircraft data with the software suite. The results confirmed that the URAF could dynamically assess risk, providing operators with visual representations of impact areas and nominal risk levels to aid in trajectory planning. The significance of this work lies in its provision of a foundational tool for real-time risk management in UTM ecosystems. By enabling dynamic risk assessment, the URAF supports the transition from restrictive visual line-of-sight operations to more complex, safety-critical autonomous flights. The framework’s modular design allows for future enhancements, such as incorporating high-fidelity failure models, real-time population density via cellular data, and comprehensive sensor inputs. This approach addresses critical safety concerns for bystanders, offering a scalable solution for integrating sUAS into shared airspace while maintaining public safety standards.

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