Aircraft trajectory prediction in terminal airspace with intentions derived from local history

Abstract

Aircraft trajectory prediction aims to estimate the future movements of aircraft in a scene, which is a crucial step for intelligent air traffic management such as capacity estimation and conflict detection. Current approaches primarily rely on inputting absolute locations, which improves the prediction accuracy but limits the model’s generalization ability to unseen environments. To bridge the gap, we propose to alternatively learn aircraft’s intentions from a repository of historical trajectories. Based on the observation that aircraft traveling through the same airspace may exhibit comparable behaviors, we utilize a location-adaptive threshold to identify nearby neighbors for a given query aircraft within the repository. The retrieved candidates are next filtered based on contextual information, such as landing time and landing direction, to eliminate less relevant components. The resulting set of nearby candidates are referred to as the local history, which emphasizes the modeling of aircraft’s local behavior. Moreover, an attention-based local history encoder is presented to aggregate information from all nearby candidates to generate a latent feature for capturing the aircraft’s intention. This latent feature is robust to normalized input trajectories, relative to the current location of the target aircraft, thus improving the model’s generalization capability to unseen areas. Our proposed intention modeling method is model-agnostic, which can be leveraged as an additional condition by any trajectory prediction model for improved robustness and accuracy. For evaluation, we integrate the intention modeling component into our previous diffusion-based aircraft trajectory prediction framework. We conduct experiments on two real-world aircraft trajectory datasets in both towered and non-towered terminal airspace. The experimental results show that our method captures various maneuvering patterns effectively, outperforming existing methods by a large margin in terms of both ADE and FDE.