Generating Synthetic Flight Tracks for Collision Risk Safety Analysis: Variational Autoencoders with a Single Seed Track

Collision Risk Models (CRM), used by regulatory authorities to approve new procedures and/or technology, assess the probability of air-to-air collisions against a Target Level of Safety (e.g. 10E-9). A key component of the CRM is a variety of plausible flight tracks. Historic flight tracks can be blended with simulated flight tracks to generate a sample of tracks for analysis, however, even using super-computers, the sample size of flight tracks is not sufficient for a narrow confidence interval for the probability of a collision.This paper describes the results of the application of an AI/ML algorithm known as Variational Autoencoders (VAE) to increase the sample size of flight tracks for CRM. 2,356 flight tracks for the Ondre 1 arrival to Runway 26R at Atlanta airport were used to train a VAE. Next a single "seed" track, representing a nominal trajectory, was used to generate 2,356 synthetic flight tracks. By properly adjusting the VAE coefficients, the generated synthetic tracks matched historic tracks with regards to mean Along Track Distance (ATD), Total Cross Track Distance (TCTD), vertical dispersion at 9nm and 6nm from the Runway, and lateral dispersion at 9nm and 6nm from the Runway. However, in all the metrics the variability in the flight tracks was significantly smaller than the variability in the historic tracks. These promising results suggest that an approach using multiple "seed tracks" could achieve the desired variability. The implications of these results and future work are discussed.