V2TATC：用于空中交通管制员态势感知的联合语音-轨迹嵌入框架与数据集
V2TATC: Joint Voice-Trajectory Embedding and Dataset for Air Traffic Controller Situational Awareness
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中文总结 AI 辅助
V2TATC是联合语音-轨迹嵌入框架，结合多模态技术，在旧金山湾区空域验证有效性，发布配对数据集并开展多类实验，助力空中交通管制员态势感知。
中文摘要 AI 辅助
随着美国国家空域系统的空中交通量持续增长，尤其是在低空空域，空中交通管制员使用的可扩展决策支持工具的需求也需要进一步开发。本文介绍了Voice-to-Trajectory for Air Traffic Control（V2TATC），这是一种联合语音通信-飞行轨迹数据嵌入框架，可作为拥挤空域中态势感知的组成部分，协助开发空中交通管制（ATC）工具，使其能实时推理自动依赖广播（ADS-B）轨迹或飞行员用自然语言表达的意图。我们表明这些数据模态并非独立，而是代表了空中飞行的飞机这一共同物理指称。V2TATC将语音指令和目标飞机的轨迹映射到单个潜在空间中的邻近点，该空间可双向查询，它结合了自监督轨迹编码器、冻结的大规模语音编码器、对比联合嵌入以及通过归一化流实现的双射提升。我们在旧金山湾区验证了V2TATC的有效性，该区域集中了主要机场，且混合了商业和通用航空低空交通。最后，我们发布了一个新颖的配对语音-轨迹数据集，并报告了跨模态检索、消融实验和潜在空间分析的结果。
英文摘要
As air traffic volumes in the National Airspace System continue to expand, in particular at low altitude, the need for scalable decision support tools used by air traffic controllers will also require more development. This article introduces Voice-to-Trajectory for Air Traffic Control, a joint voice communication-flight trajectory data embedding framework, that can be a component of situational awareness in congested airspaces, and assist the development of tools for ATC as they reason in real-time over Automatic Dependent Surveillance-Broadcast trajectories, or the intent expressed by pilots in natural language. We show that these data modalities are not independent and represent a common physical referent: an aircraft flying through the airspace. V2TATC maps a voice instruction and the trajectory of the addressed aircraft to nearby points in a single latent space that can be queried in both directions. It combines a self-supervised trajectory encoder, a frozen speech encoder, a contrastive joint embedding, and a bijective lifting via normalizing flows. We demonstrate V2TATC's effectiveness on the San Francisco Bay Area, for its concentration of major airports, and its mix of commercial and general aviation traffic. Lastly, we release a novel paired voice-trajectory dataset, and report experiments on cross-modal retrieval, ablations, and latent-space analysis.
发表机构
- University of California, Berkeley（加利福尼亚大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。
