面向空中交通管理中AI辅助飞行规划的决策保障层
Toward a Decision-Assurance Layer for AI-Assisted Flight Planning in Air Traffic Management
浏览论文内容
中文总结 AI 辅助
本文提出AI信任与保障层（ATAL），一种模型无关的决策保障架构，通过评估语义稳定性、运营一致性和规范性约束，将AI飞行规划输出映射为决策就绪等级（DRL），以在影响运营前识别不可靠输出，并适用于其他安全关键领域。
中文摘要 AI 辅助
生成式人工智能正越来越多地被非正式地用于空中交通管理（ATM）中，执行诸如飞行计划生成、轨迹解释和约束检查等任务。尽管这些工具可以减轻工作负担并加速规划过程，但其非确定性的输出在人在回路环境中带来了安全和运营风险。本文提出了AI信任与保障层（ATAL），这是一种与模型无关的决策保障架构，用于评估AI生成的飞行规划输出是否足够可靠以供运营使用。ATAL结合了提示变化下的语义稳定性、结构化输出的运营一致性以及针对领域规则的规范性约束验证，并将这些信号映射为供人类操作员使用的决策就绪等级（DRL）。一项受ATM启发的实验研究表明，不安全、不一致或误导性的输出可以在影响飞行计划验证或执行之前被识别出来。尽管在航空领域进行了演示，该框架也可迁移至其他在监管约束下需要人类监督的安全关键决策支持领域。
英文摘要
Generative AI is increasingly being used informally in Air Traffic Management (ATM) for tasks such as flight plan generation, trajectory interpretation, and constraint checking. Although these tools can reduce workload and accelerate planning, their non-deterministic outputs create safety and operational risks in human-in-the-loop settings. This paper proposes the AI Trust and Assurance Layer (ATAL), a model-agnostic decision assurance architecture that evaluates whether AI-generated flight-planning outputs are sufficiently reliable for operational use. ATAL combines semantic stability under prompt variation, operational consistency of structured outputs, and normative constraint validation against domain rules, and maps these signals to a Decision Readiness Level (DRL) for human operators. An ATM-inspired experimental study shows how unsafe, inconsistent, or misleading outputs can be identified before influencing flight-plan validation or execution. Although demonstrated in aviation, the framework is also transferable to other safety-critical decision-support domains that require human oversight under regulatory constraints.
发表机构
- George Mason University（乔治梅森大学）
- Tecnológico de Monterrey（蒙特雷理工学院）
- Department of Airspace Control (DECEA)（空域控制部（DECEA））
机构由 AI 辅助整理，请以论文原文为准。
