Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach

📄 arXiv: 2608.18017v1 📥 PDF

作者: Lu Xu, Xu Li, Linjiang Zheng, Fan Li, Riquan Zhang, Jiaxing Shang

分类: cs.AI

发布日期: 2026-08-18

备注: 14 pages, 6 figures, submitted to IEEE Transactions on Intelligent Transportation Systems

💡 一句话要点

提出FlightLLM以解决飞行安全事件解释问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 飞行安全 可解释人工智能 大型语言模型 特征工程 语义离散化 CatBoost 对比少量学习 航空数据分析

📋 核心要点

  1. 现有可解释AI技术在飞行安全事件分析中面临模式不一致、分类能力有限和领域知识缺乏等挑战。
  2. 本文提出FlightLLM，通过特征工程和语义离散化模块，结合CatBoost和对比少量学习策略，提升了模型的解释能力和分类性能。
  3. 在对704个真实A320飞行样本的实验中，FlightLLM不仅实现了良好的分类效果，还能生成直接且合理的事件原因解释。

📝 摘要（中文）

提高飞行安全性需要准确检测风险事件，并清晰解释其背后的原因，尤其是飞行员的控制行为。现有的可解释人工智能技术往往需要大量领域知识，难以转化为操作上有意义的解释。本文提出FlightLLM，一种基于大型语言模型的先验引导语义分析方法，旨在解决这一问题。通过特征工程和语义离散化模块，本文将抽象的数值模式转化为更适合语言推理的定性描述，并结合CatBoost作为统计专家，采用对比少量学习策略以应对数据稀缺。实验结果表明，FlightLLM在704个真实A320飞行样本上实现了竞争力的分类性能，并生成了合理的事件原因解释。

🔬 方法详解

问题定义：本文旨在解决飞行安全事件的解释问题，现有方法在生成可操作的解释时常常依赖于大量领域知识，导致解释的有效性和可用性不足。

核心思路：提出FlightLLM，通过特征工程和语义离散化将数值数据转化为语言可理解的形式，并结合CatBoost作为分类器，增强模型的解释能力。

技术框架：整体架构包括特征工程模块、语义离散化模块、CatBoost分类器和结构化提示设计，形成一个完整的飞行安全分析流程。

关键创新：最重要的创新在于将统计描述与物理意义相结合，利用语义离散化将抽象数据转化为定性描述，并通过对比少量学习策略解决数据稀缺问题。

关键设计：在特征工程中，结合了统计特征和飞行指标，语义离散化模块负责将数值模式转化为语言描述，CatBoost的预测结果作为先验信息注入提示中，增强了模型的分类能力。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，FlightLLM在704个真实A320飞行样本上实现了优异的分类性能，具体表现为在复杂的硬着陆事件中，模型不仅能够准确分类，还能生成合理的事件原因解释，提升了传统方法的有效性。

🎯 应用场景

该研究具有广泛的应用潜力，尤其在航空安全分析、飞行数据监控和飞行员培训等领域。通过提供清晰的事件解释，能够帮助航空公司和监管机构更好地理解和应对飞行安全风险，提升整体飞行安全性。

📄 摘要（原文）

Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the level of pilot control behavior. Existing explainable AI techniques, such as feature importance maps, often require considerable domain knowledge to translate them into operationally meaningful explanations. Large Language Models (LLMs), which excel at language reasoning, bring a promising solution to this issue. However, applying LLMs in this domain presents key challenges such as modal inconsistency, limited classification ability, scarcity of task-specific data for fine-tuning, and lack of domain knowledge. To overcome these challenges, we propose FlightLLM, a prior-guided semantic LLM-based approach for interpretable flight safety analysis. Specifically, we first perform feature engineering to address modal inconsistency, combining statistical descriptors with physically meaningful flight indicators. This representation is further processed by a Semantic Discretization module, which converts abstract numerical patterns into qualitative descriptions that are more compatible with language reasoning. In addition, since LLMs are not inherently strong classifiers, CatBoost is incorporated as a statistical expert, and its prediction results are injected into the prompt as prior guidance. A contrastive few-shot learning strategy is further adopted to compensate for limited data. Finally, we design structured prompts to embed aviation-specific knowledge into the inference process. Using hard landing, a representative risk event with complex causal mechanisms, as an anchor point, we evaluate FlightLLM on a dataset of 704 real-world A320 flight samples. Experimental results show that the proposed approach achieves competitive classification performance while generating direct and reasonable explanations for event causes.