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Deep Learning Framework for Trajectory Prediction and In-time Prognostics in the Terminal Airspace

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posted on 2022-10-06, 13:09 authored by Varun S SudarsananVarun S Sudarsanan

Terminal airspace around an airport is the biggest bottleneck for commercial operations in the National Airspace System (NAS). In order to prognosticate the safety status of the terminal airspace, effective prediction of the airspace evolution is necessary. While there are fixed procedural structures for managing operations at an airport, the confluence of a large number of aircraft and the complex interactions between the pilots and air traffic controllers make it challenging to predict its evolution. Modeling the high-dimensional spatio-temporal interactions in the airspace given different environmental and infrastructural constraints is necessary for effective predictions of future aircraft trajectories that characterize the airspace state at any given moment. A novel deep learning architecture using Graph Neural Networks is proposed to predict trajectories of aircraft 10 minutes into the future and estimate prog?nostic metrics for the airspace. The uncertainty in the future is quantified by predicting distributions of future trajectories instead of point estimates. The framework’s viability for trajectory prediction and prognosis is demonstrated with terminal airspace data from Dallas Fort Worth International Airport (DFW). 

History

Degree Type

  • Doctor of Philosophy

Department

  • Aeronautics and Astronautics

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Daniel A. DeLaurentis

Additional Committee Member 2

Inseok Hwang

Additional Committee Member 3

Dengfeng Sun

Additional Committee Member 4

Jitesh Panchal

Additional Committee Member 5

Peter F. Kostiuk