Research on drowsiness detection in UAV operators based on the random decision forest method
Year: 2026
Type: article
Abstract: Drowsiness poses a significant risk in safety-critical operations such as operating unmanned aerial vehicles (UAV). While behavioral indicators like eye closure and head pose are effective for detection, the interpretability of complex models remains a challenge. This work employs a Random Forest model not merely as a classifier, but as a diagnostic tool to analyze dataset biases and feature correlations in drowsiness detection. Using established benchmarks, we demonstrate how this interpretable framework provides actionable insight into feature importance and model decision boundaries. The analysis offers a method to audit training data and informs the more reliable application of high-performance black-box systems. Our approach underscores the value of model transparency for developing robust, trustworthy drowsiness detection in operational environments.
Source: Scientific Reports
Institution: Military University of Technology in Warsaw
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7130311343