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Pilots operating aircraft in un-towered airspace rely on their situationalawareness and prior knowledge to predict the future trajectories of otheragents. These predictions are conditioned on the past trajectories of otheragents, agent-agent social interactions and environmental context such asairport location and weather. This paper provides a dataset,$\textit{TrajAir}$, that captures this behaviour in a non-towered terminalairspace around a regional airport. We also present a baseline socially-awaretrajectory prediction algorithm, $\textit{TrajAirNet}$, that uses the datasetto predict the trajectories of all agents. The dataset is collected for 111days over 8 months and contains ADS-B transponder data along with thecorresponding METAR weather data. The data is processed to be used as abenchmark with other publicly available social navigation datasets. To the bestof authors' knowledge, this is the first 3D social aerial navigation datasetthus introducing social navigation for autonomous aviation.$\textit{TrajAirNet}$ combines state-of-the-art modules in social navigation toprovide predictions in a static environment with a dynamic context. Both the$\textit{TrajAir}$ dataset and $\textit{TrajAirNet}$ prediction algorithm areopen-source. The dataset, codebase, and video are available athttps://theairlab.org/trajair/, https://github.com/castacks/trajairnet, andhttps://youtu.be/elAQXrxB2gw respectively.

Predicting Like A Pilot: Dataset and Method to Predict Socially-Aware Aircraft Trajectories in Non-Towered Terminal Airspace