LeRAAT: LLM-Enabled Real-Time Aviation Advisory Tool

• URL: http://arxiv.org/abs/2503.16477v1
• Date: Wed, 05 Mar 2025 05:34:15 GMT
• Title: LeRAAT: LLM-Enabled Real-Time Aviation Advisory Tool
• Authors: Marc R. Schlichting, Vale Rasmussen, Heba Alazzeh, Houjun Liu, Kiana Jafari, Amelia F. Hardy, Dylan M. Asmar, Mykel J. Kochenderfer,
• Abstract summary: This paper introduces LeRAAT, a framework that integrates large language models (LLMs) with the X-Plane flight simulator to deliver real-time, context-aware pilot assistance.<n>The system uses live flight data, weather conditions, and aircraft documentation to generate recommendations aligned with aviation best practices.
• Score: 29.306750550243894
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In aviation emergencies, high-stakes decisions must be made in an instant. Pilots rely on quick access to precise, context-specific information -- an area where emerging tools like large language models (LLMs) show promise in providing critical support. This paper introduces LeRAAT, a framework that integrates LLMs with the X-Plane flight simulator to deliver real-time, context-aware pilot assistance. The system uses live flight data, weather conditions, and aircraft documentation to generate recommendations aligned with aviation best practices and tailored to the particular situation. It employs a Retrieval-Augmented Generation (RAG) pipeline that extracts and synthesizes information from aircraft type-specific manuals, including performance specifications and emergency procedures, as well as aviation regulatory materials, such as FAA directives and standard operating procedures. We showcase the framework in both a virtual reality and traditional on-screen simulation, supporting a wide range of research applications such as pilot training, human factors research, and operational decision support.

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