LeRAAT: LLM-Enabled Real-Time Aviation Advisory Tool
This is an incremental application of existing methods to a new domain, aiming to support pilots, trainers, and researchers in aviation decision-making.
The paper tackles the problem of providing real-time, context-specific assistance to pilots during aviation emergencies by introducing LeRAAT, a framework that integrates large language models with flight simulators to generate tailored recommendations, though no concrete performance numbers are reported.
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.