Toward a Decision-Assurance Layer for AI-Assisted Flight Planning in Air Traffic Management

2026-09-11

AI Summary

本文提出AI信任与保证层（ATAL），通过评估AI生成的飞行计划输出的可靠性，解决航空交通管理中AI辅助决策的安全性和操作风险问题。

Abstract

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.

Problem

• Air Traffic Management
• Generative AI
• Safety Risks
• Operational Risks
• Decision Assurance

Innovation

• AI Trust and Assurance Layer
• Decision Readiness Level
• semantic stability
• operational consistency
• normative constraint validation