用混合模型与序列模型提升航迹预测精度，支持每秒一帧的高分辨率输出。
Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace
- 结合混合模型与序列网络，缓解误差累积与维度下降问题。
- 在终端空域数据集上，长程预测误差显著低于现有方法。
- 支持每秒1步的高时间分辨率，更贴近真实飞行轨迹。
飞行轨迹预测具有重要研究价值。本文提出一种数据驱动的学习框架，融合混合模型与基于seq2seq的神经网络，在保留其预测与特征提取能力的同时，缓解了误差传播和维度降低带来的普遍挑战。训练完成后，该模型在给定历史轨迹与上下文信息条件下，能显著提升长步长预测精度。通过与真实轨迹对比评估，结果表明：所提方法在终端空域飞行轨迹数据集上优于现有最先进方法。生成的轨迹具备更高时间分辨率（每秒1步，对比0.1步），且更接近真实轨迹。
原文摘要 · Abstract (English)
Predicting flight trajectories is a research area that holds significant merit. In this paper, we propose a data-driven learning framework, that leverages the predictive and feature extraction capabilities of the mixture models and seq2seq-based neural networks while addressing prevalent challenges caused by error propagation and dimensionality reduction. After training with this framework, the learned model can improve long-step prediction accuracy significantly given the past trajectories and the context information. The accuracy and effectiveness of the approach are evaluated by comparing the predicted trajectories with the ground truth. The results indicate that the proposed method has outperformed the state-of-the-art predicting methods on a terminal airspace flight trajectory dataset. The trajectories generated by the proposed method have a higher temporal resolution(1 timestep per second vs 0.1 timestep per second) and are closer to the ground truth.
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