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. 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.

@article{schlichting2025_2503.16477,
  title={ LeRAAT: LLM-Enabled Real-Time Aviation Advisory Tool },
  author={ Marc R. Schlichting and Vale Rasmussen and Heba Alazzeh and Houjun Liu and Kiana Jafari and Amelia F. Hardy and Dylan M. Asmar and Mykel J. Kochenderfer },
  journal={arXiv preprint arXiv:2503.16477},
  year={ 2025 }
}