Bayesian Spatio-Temporal grAph tRansformer network (B-STAR) for multi-aircraft trajectory prediction

Abstract

Multi-Agent Trajectory Prediction is a critical and challenging component across different safety–critical engineering applications, e.g., autonomous driving and flight systems. Trajectory prediction tools are required for the next-generation air transportation system (NextGen). In practice, the prediction of aircraft trajectories needs to consider the impact of various sources, such as environmental conditions, pilot/controller behaviors, and potential conflicts with nearby aircraft. Huge uncertainties associated with these factors lead to the untrustworthiness of a deterministic trajectory prediction model. Moreover, the safety assurance in the near-terminal area is of specific interest due to the increased airspace complexity, where the instrument/visual flight rules are applied. In this work, we propose the Bayesian Spatio-Temporal grAph tRansformer (B-STAR) architecture to model the spatial and temporal relationship of multiple agents under uncertainties. It is shown that the proposed B-STAR achieves state-of-the-art performance on the ETH/UCY pedestrian dataset with UQ competence. Then, multi-aircraft near-terminal interactive trajectory prediction model is trained and validated with real-world flight recording data. The sensitivity study on the prediction/observation horizon and the graph neighboring distance threshold are performed. The code is available at https://github.com/ymlasu/para-atm-collection/tree/master/air-traffic-prediction/MultiAircraftTP.