Real-time Prediction of UAV Pilot Operational Errors and Adaptive Threshold Optimization under Temporal Models

Operational errors by UAV pilots have become a critical bottleneck constraining industry safety. Existing prediction methods mostly adopt unified models while neglecting individual pilot capability differences. This study proposes a real-time prediction framework integrating pilot adaptability features with operational temporal data. A dual-channel network architecture separately extracts temporal and adaptability features, utilizing cross-modal attention for deep fusion. An adaptive threshold algorithm dynamically adjusts warning parameters based on pilot capability levels. Experiments on 665 pilots demonstrate 89.7% accuracy, 0.928 AUC, and 3.2-second average warning time. Complex reaction, control function, and motion stability are identified as key predictive indicators, contributing 28.3%, 25.6%, and 19.5% respectively. The adaptive threshold strategy reduces false negative rates for low-adaptability pilots by 50.4% and false positive rates for high-adaptability pilots by 64.6%. Leave-one-pilot-out validation confirms generalization capability (86.3% accuracy). These findings support transforming UAV safety management from post-incident analysis to proactive prevention and from unified standards to personalized adaptation.