用AI自动扮演飞行员和空管员，提升空管培训效率与准确性
ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots
- 用本地化语音模型自动生成飞行员和空管员对话响应
- 新加坡口音航空语音识别错误率从107.8%降至23.45%
- 可自动评估学员通信的准确、简洁与完整度，适合空管培训场景
空中交通管制员（ATCO）对保障空中交通的安全、有序和高效至关重要，但培训受限于依赖专业真人扮演飞行员和管制员的模拟飞行员。现有自动化方案多基于西方语音模型，在新加坡操作语境下表现不佳，现成系统在新加坡口音航空语音上的词错误率（WER）高达107.80%。我们提出ASTRA，一个端到端的下一代空管培训模拟器，通过语音识别、指令理解与本地适配语音模型生成飞行员与管制员回应，实现角色自动化。其微调后的自动语音识别（ASR）管道将WER降低至23.45%，显著优于现有方法。除交通模拟外，ASTRA还集成AI辅助评估框架，从准确性、简洁性和完整性三方面评估学员无线电通信，优化后得分分别为91.7%、88.2%和86.9%。基于DSPy与Unsloth等开源工具，该方案实现了可扩展、标准化的空管员评估，大幅降低导师负担。
原文摘要 · Abstract (English)
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.
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