LeRAAT: LLM-Enabled Real-Time Aviation Advisory Tool

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. To help research the effects of bringing AI into an aircraft cockpit, 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. LeRAAT can support a wide range of future research applications such as pilot training, human factors, and operational decision support.

Authors

• Marc R. Schlichting
• Vale Rasmussen
• Heba Alazzeh
• Houjun Liu
• Kiana Jafari Meimandi
• Amelia F. Hardy
• Dylan M. Asmar
• Mykel J. Kochenderfer

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