X-VORTEX：用于尾涡轨迹预测的时空对比学习
X-VORTEX: Spatio-Temporal Contrastive Learning for Wake Vortex Trajectory Forecasting
AI总结：
X-VORTEX通过时空对比学习方法，利用未标注数据高效预测尾涡轨迹，解决了传感器稀疏性和时间变化涡旋动力学问题。
摘要：
尾涡是飞机产生的强、相干空气湍流，对空交通管理的安全性和容量构成重大挑战。从LiDAR测量中跟踪尾涡随时间移动、减弱和消散仍然困难，因为扫描稀疏，涡旋特征在大气湍流和不稳定性下逐渐消失，且点注标注成本过高。现有方法大多将每个扫描视为独立的全监督分割问题，忽略了时间结构，无法扩展到实践中收集的大量未标记档案。我们提出了X-VORTEX，一种基于Augmentation Overlap理论的时空对比学习框架，从未标记的LiDAR点云序列中学习物理感知的表示。X-VORTEX解决两个核心挑战：传感器稀疏性和时间变化的涡旋动力学。它通过将弱扰动序列与通过时间下采样和空间遮蔽生成的强增强对应物结合，从同一基础飞行事件构建配对输入，鼓励模型在缺失帧和部分观测中对齐表示。在架构上，时间分布的几何编码器提取每扫描的特征，序列聚合器建模在变量长度序列中演变的涡旋状态。我们在超过一百万次LiDAR扫描的真实世界数据集上进行评估。X-VORTEX在仅使用监督基线所需1%的标注数据的情况下实现了优越的涡旋中心定位，且学习的表示支持准确的轨迹预测。
英文摘要：
Wake vortices are strong, coherent air turbulences created by aircraft, and they pose a major safety and capacity challenge for air traffic management. Tracking how vortices move, weaken, and dissipate over time from LiDAR measurements is still difficult because scans are sparse, vortex signatures fade as the flow breaks down under atmospheric turbulence and instabilities, and point-wise annotation is prohibitively expensive. Existing approaches largely treat each scan as an independent, fully supervised segmentation problem, which overlooks temporal structure and does not scale to the vast unlabeled archives collected in practice. We present X-VORTEX, a spatio-temporal contrastive learning framework grounded in Augmentation Overlap Theory that learns physics-aware representations from unlabeled LiDAR point cloud sequences. X-VORTEX addresses two core challenges: sensor sparsity and time-varying vortex dynamics. It constructs paired inputs from the same underlying flight event by combining a weakly perturbed sequence with a strongly augmented counterpart produced via temporal subsampling and spatial masking, encouraging the model to align representations across missing frames and partial observations. Architecturally, a time-distributed geometric encoder extracts per-scan features and a sequential aggregator models the evolving vortex state across variable-length sequences. We evaluate on a real-world dataset of over one million LiDAR scans. X-VORTEX achieves superior vortex center localization while using only 1% of the labeled data required by supervised baselines, and the learned representations support accurate trajectory forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
