Research on drowsiness detection in UAV operators based on the random decision forest method

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.