The unregulated growth of drones has introduced safety challenges in controlled Class B airspace and near airports. Using geospatial processing and large-language modeling (LLM) techniques, the FAA-reported drone intrusion database, which contains unstructured pilots’ logs, is transformed into structured, engineering-ready datasets. These datasets are used to determine UAS locations.
Bayesian statistical modeling was used to determine (1) the probability of Class B drone intrusions and (2) the risk levels. The severity of drone intrusions was assessed using Natural Language Processing (NLP) applied to structured pilot logs. Risk was computed by combining the Bayesian model’s outputs with the NLP classifier to produce a comprehensive, data-driven risk analysis. We show that the severity of drone intrusions can be predicted by extracting latent features from pilot and ATC logs using NLP and incorporating them into a probabilistic forecasting framework. To date, this is the first model that forecasts drone intrusion severity using NLP-augmented Bayesian models.
Jasenka Rakas, Ph.D., is a faculty member in the Civil and Environmental Engineering Department and in the Mechanical Engineering Department’s Aerospace Engineering program, and is affiliated faculty with the Jacobs Institute for Design Innovation at the University of California (UC) Berkeley. She is Deputy Director of the UC Berkeley National Center of Excellence for Aviation Operations Research (NEXTOR III), founder of the UC Berkeley Airport Design Studio and Aviation Futures Research Lab, and Co-chair of the Sustainable Aviation Symposium. Her research interests include aviation systems safety, reliability, and resilience; aviation infrastructure performance; and their interactions with the environment. She has received numerous awards, including seven national competition awards from the FAA/ACRP.
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