Context-aware Aircraft Trajectory Prediction with Diffusion Models

Abstract

Aircraft trajectory prediction aims to estimate the future movements of the aircraft, which is a crucial step for air traffic management such as capacity estimation and conflict detection. In this paper, we present a context-aware trajectory prediction method, which generates the future movements based on both the aircraft's past status and the contextual information such as the pilot and controller intent and the environmental conditions. The proposed framework consists of 1) a Trajectory Encoder that captures the history behaviors and the social interactions of the aircraft, 2) a Context Encoder that extracts latent features from contextual information, and 3) a Transformer-based Decoder that generates future trajectories based on a diffusion model. Specifically, we model the trajectory prediction as the reverse diffusion process where we first gradually add noise to the ground-truth trajectory and then train a neural network to learn the reverse of this diffusion process conditioned on the output of the trajectory encoder and the context encoder. We conduct experiments on real-world aircraft trajectories collected at Singapore Changi Airport in December 2019, which correspond to one-week ADS-B data before the start of the COVID-19 pandemic. The experimental results show that our proposed approach outperforms existing methods by a significant margin.