Uncertainty Propagation in Pre-Flight Prediction of Unmanned Aerial Vehicle Separation Violations

Current forecasts on the future of aeronautics suggest an increasing number of unmanned aerial vehicles entering the low-altitude airspace in the next decades. Small vehicles for package delivery as well as larger vehicles for urban air mobility will change the dynamics of the airspace, increasing density of operations both in time, i.e. high number of take-off and landings per unit time, and in space, operating in dense urban environment. This scenario poses challenges to the current approach to air traffic control, and large efforts from academia, industry and regulatory bodies are dedicated to the development of new traffic management strategies that leverage higher computing and simulating capabilities avail-able today. In this paper, we propose a simple look-ahead approach to predict potential minimum separation violations at the strategic level, that is before vehicles start flying, depending on the predefined 4D trajectories and uncertainty affecting the wind acting along those routes. Wind forecast data are provided by True Weather Solutions, Inc. on a sparse grid of latitude/longitude coordinates. We then interpolate the sparse data using Gaussian process regression to obtain an estimate of wind speed and direction, while uncertainty affecting the expected ground speed is propagated through error intervals. The approach allows the prediction of aircraft separation as a function of time, highlighting potential safety violations that would go undetected if uncertainty affecting the expected 4D trajectories was not considered. The paper will also discuss issues related to accuracy and scalability of the approach to multiple vehicle operations.