V2TATC: A Joint Voice-Trajectory Embedding Framework and Dataset for Air Traffic Controller Situational Awareness

• URL: http://arxiv.org/abs/2608.28981v1
• Date: Sat, 29 Aug 2026 01:22:52 GMT
• Title: V2TATC: A Joint Voice-Trajectory Embedding Framework and Dataset for Air Traffic Controller Situational Awareness
• Abstract summary: Voice-to-Trajectory for Air Traffic Control (V2TATC) is a joint voice communication-flight trajectory data embedding framework.<n>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.<n>We show that V2TATC's effectiveness on the San Francisco Bay Area, for its concentration of major airports, and its mix of commercial and general aviation low altitude traffic.
• Score: 39.21497179369917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As air traffic volumes in the National Airspace System continue to expand, in particular in the low altitude airspaces, 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 large-scale 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 low altitude traffic. Lastly, we release a novel paired voice-trajectory dataset, and report experiments on cross-modal retrieval, ablations, and latent-space analysis.

Related papers

• DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction [54.94692733670454]
  DESCENT is a transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints.<n>Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context.<n>Our evaluations on the Amelia-10 benchmark demonstrate significant performance improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-08-26T16:49:50Z)
• Unlocking air traffic flow prediction through microscopic aircraft-state modeling [32.3411287446595]
  We present AeroSense, a state-to-flow modeling framework that predicts future traffic flow directly from instantaneous airspace situations.<n> Experiments on a large-scale real-world dataset show that AeroSense consistently improves predictive accuracy over aggregation-based forecasting approaches.
  arXiv  Detail & Related papers  (2026-05-11T07:04:55Z)
• ASCENT: Transformer-Based Aircraft Trajectory Prediction in Non-Towered Terminal Airspace [54.94692733670454]
  ASCENT is a lightweight transformer-based model for multi-modal 3D aircraft trajectory forecasting.<n>It integrates domain-aware 3D coordinate normalization and parameterized predictions.
  arXiv  Detail & Related papers  (2026-03-17T14:16:10Z)
• Airspace-aware Contingency Landing Planning [5.173672380708639]
  This paper develops a real-time, search-based aircraft contingency landing planner that minimizes traffic disruptions while accounting for ground risk.<n> Historical Automatic Dependent Surveillance-Broadcast (ADS-B) data are processed to estimate air traffic density.<n>A low-latency computational geometry algorithm generates proximity-based heatmaps around high-risk corridors and restricted regions.
  arXiv  Detail & Related papers  (2026-02-06T01:04:10Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• Combining Textual and Spectral Features for Robust Classification of Pilot Communications [2.9346090159291283]
  This paper presents a novel dual pipeline machine learning framework that classifies pilot radio communications using both textual and spectral features.<n>To our knowledge, this is the first system to classify operational aircraft intent using a dual-pipeline ML framework on real-world air traffic audio.
  arXiv  Detail & Related papers  (2025-09-11T16:43:10Z)
• Learning to Explain Air Traffic Situation [0.0]
  We propose a machine learning-based framework for explaining air traffic situations.<n>Specifically, we employ a Transformer-based multi-agent trajectory model that encapsulates both the Previous-temporal movement of aircraft and social interaction between them.<n>This provides explainable insights into how air traffic controllers perceive and understand the traffic situation.
  arXiv  Detail & Related papers  (2025-02-15T11:03:47Z)
• Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information [72.20674534231314]
  Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO) The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. We propose a new call-sign recognition and understanding (CRU) system that addresses this issue. The recognizer is trained to identify call-signs in noisy ATC transcripts and convert them into the standard International Civil Aviation Organization (ICAO) format.
  arXiv  Detail & Related papers  (2022-04-13T11:30:42Z)
• Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
  Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
  arXiv  Detail & Related papers  (2021-10-28T14:12:10Z)
• A Simplified Framework for Air Route Clustering Based on ADS-B Data [0.0]
  This paper presents a framework that can support to detect the typical air routes between airports based on ADS-B data. As a matter of fact, our framework can be taken into account to reduce practically the computational cost for air flow optimization.
  arXiv  Detail & Related papers  (2021-07-07T08:55:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.