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

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.

Call signs are safety-critical entities in air traffic control (ATC) communications because they identify the target aircraft of each spoken instruction. This paper presents ATCCaps, a call-sign-aware ATC speech dataset with caption-level audio-text supervision. Built from real ATC radiotelephony recordings, ATCCaps contains 202.94 hours of curated audio, 170,385 utterances, and 922 unique normalized call signs. The construction pipeline combines confidence-aware transcript parsing, ADS-B-derived call-sign metadata, call-sign normalization, rule-based quality filtering, and LLM-assisted caption generation. Each retained sample is paired with transcript descriptions, call-sign descriptions, and ATC-style captions, supporting ASR evaluation, call-sign matching, and call-sign-aware audio-text retrieval. We further characterize ATCCaps through split statistics, call-sign coverage, seen/unseen call-sign analysis, filtering audits, and caption quality evaluation. The evaluation subset is derived from the human-annotated ATCO2-test-set, enabling reference evaluation with manual transcripts. Results show that ATCCaps provides scalable audio-grounded call-sign supervision, while caption analysis highlights the need to explicitly validate call-sign and numeric fidelity. Reference ASR and CLAP-based baselines demonstrate the usability of ATCCaps for call-sign-aware ATC speech modeling.

Air Traffic Control Operators (ATCOs) are vital in ensuring the safe, orderly, and efficient flow of air traffic, yet training capacity is constrained by reliance on specialized human trainers known as simpilots, who must role-play both pilots and ATCOs in a simulated airspace. Existing automated solutions rely on Western-centric speech models that perform poorly in Singaporean operational contexts, with off-the-shelf systems exhibiting Word Error Rates (WER) of up to 107.80% on Singaporean-accented aviation speech. We introduce ASTRA, an end-to-end training simulator that automates these simpilot roles through a pipeline that transcribes ATCO speech, interprets instructions, and generates appropriate pilot and ATCO responses using locally adapted voice models. Our fine-tuned Automatic Speech Recognition (ASR) pipeline reduces WER to 23.45%, substantially outperforming existing approaches in this domain. Beyond traffic simulation, ASTRA incorporates an AI-assisted performance evaluation framework that assesses trainee radiotelephony communications across accuracy, brevity, and completeness, achieving post-optimization scores of 91.7%, 88.2%, and 86.9%, respectively. Built on open-source foundations such as DSPy and Unsloth, this approach enables scalable, standardized ATCO assessment while reducing instructor workload.