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. Specifically, the FlightBERT++ 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 with 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. Moreover, a differential prompted decoder is proposed to enhance the capability of the differential predictions by leveraging the stationarity of the differential sequence. The experimental results on a real-world dataset demonstrated that the FlightBERT++ outperformed the competitive baselines in both FTP performance and computational efficiency.

翻译：飞行轨迹预测（FTP）是空中交通管制（ATC）中的关键任务，可辅助空中交通管制员更安全高效地管理空域。现有方法通常以自回归方式执行多时间跨度FTP任务，因此存在误差累积和效率低下的问题。本文提出一种名为FlightBERT++的新型框架，旨在：i）以非自回归方式直接预测多时间跨度飞行轨迹；ii）改进FlightBERT中二进制编码（BE）表示的局限性。具体而言，FlightBERT++采用通用编码器-解码器架构实现，其中编码器从历史观测中学习时空模式，解码器预测未来时间跨度的飞行状态。与传统架构相比，本文专门设计了创新的时间跨度感知上下文生成器以考虑先验时间跨度信息，从而进一步实现非自回归多时间跨度预测。此外，提出差分提示解码器，通过利用差分序列的平稳性增强差分预测能力。在真实数据集上的实验表明，FlightBERT++在FTP性能和计算效率方面均优于竞争基线方法。