发表机构
University of California, Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FlowATC提出一种仅基于历史ADS-B轨迹训练的流匹配架构，用于空中交通轨迹预测，通过序列修复生成分布，在参数匹配时优于扩散模型和CVAE基线，并支持概率占用估计。
AI 中文摘要
构建面向下一代空中交通管制的准确决策支持工具，需要稳健的轨迹预测模型。我们提出了一种流匹配架构，该架构仅使用历史飞机轨迹进行训练，无需航线标签或航图监督。模型在旧金山湾区收集的115万个自动相关监视-广播轨迹窗口上进行训练，生成的飞机轨迹分布与历史交通高度吻合，再现了旧金山机场周围的已知空域结构，例如SFO公布的NIITE FOUR离场程序的形状。我们的模型直接基于原生、不规则的ADS-B采样间隔进行训练。轨迹预测被建模为序列修复任务，使用块因果Transformer，在观测历史条件下，通过条件流匹配或去噪扩散概率模型对未来的状态令牌进行去噪。我们将我们的架构与恒定速度、确定性长短期记忆网络和条件变分自编码器基线进行了比较。在参数数量匹配的情况下，CFM在minADE@20上比DDPM高出11-26%，且两种生成目标均比CVAE基线高出31-41%。我们进一步表明，误差随预测时域的增加而平缓退化，并且在时间降采样数据上重新训练时，该架构依然有效。最后，我们采样K个独立的补全结果，生成空间概率占用估计，可作为下游冲突风险估计的输入。
英文摘要
Building accurate decision-support tools for next-generation air traffic control requires robust trajectory prediction models. We present a flow-matching architecture trained exclusively on historical aircraft trajectories, with no route labels or chart supervision. Trained on 1.15 million Automatic Dependent Surveillance-Broadcast trajectory windows collected over the San Francisco Bay Area, the model generates aircraft trajectory distributions that closely match historical traffic, reproducing known airspace structure around San Francisco Airport such as the shape of SFO's published NIITE FOUR departure procedure. Our model is trained directly on the native, irregular ADS-B sampling interval. Trajectory prediction is cast as sequence inpainting using a block-causal Transformer that denoises future state tokens conditioned on the observed history using Conditional Flow Matching or Denoising Diffusion Probabilistic Models. We compare our architecture against constant-velocity, deterministic-Long Short Term Memory, and Conditional Variational Autoencoders baselines. At matched parameter count, CFM outperforms DDPM by 11-26% in minADE@20, and both generative objectives surpass the CVAE baseline by 31-41%. We further show that the error degrades gracefully with prediction horizon, and the architecture remains effective when retrained on temporally decimated feeds. Lastly, we sample $K$ independent completions, yielding spatial probabilistic occupancy estimates that can serve as input to downstream conflict-risk estimation.
