A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework with Gray Code Representation

• URL: http://arxiv.org/abs/2305.01658v4
• Date: Mon, 24 Jun 2024 11:34:19 GMT
• Title: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework with Gray Code Representation
• Abstract summary: A novel framework, called FlightBERT++, is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way. The proposed framework is implemented by a generalized encoder-decoder architecture. The experimental results demonstrate that the proposed framework outperformed the competitive baselines in both FTP performance and computational efficiency.
• Score: 11.29608316753389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flight Trajectory Prediction (FTP) is an essential task in Air Traffic Control (ATC), which can assist air traffic controllers in managing airspace more safely and efficiently. Existing approaches generally perform multi-horizon FTP tasks in an autoregressive manner, thereby suffering from error accumulation and low-efficiency problems. In this paper, a novel framework, called FlightBERT++, is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way, and ii) improve the limitation of the binary encoding (BE) representation in the FlightBERT framework. Specifically, the proposed framework is implemented by a generalized encoder-decoder architecture, in which the encoder learns the temporal-spatial patterns from historical observations and the decoder predicts the flight status for the future horizons. Compared to conventional architecture, an innovative horizon-aware contexts generator is dedicatedly designed to consider the prior horizon information, which further enables non-autoregressive multi-horizon prediction. Additionally, the Gray code representation and the differential prediction paradigm are designed to cope with the high-bit misclassifications of the BE representation, which significantly reduces the outliers in the predictions. Moreover, a differential prompted decoder is proposed to enhance the capability of the differential predictions by leveraging the stationarity of the differential sequence. Extensive experiments are conducted to validate the proposed framework on a real-world flight trajectory dataset. The experimental results demonstrated that the proposed framework outperformed the competitive baselines in both FTP performance and computational efficiency.

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