Real-Time Prediction of UAV Pilot Operational Errors and Adaptive Threshold Optimization Under Temporal Models

Abstract

Operational errors by UAV pilots have become a critical bottleneck constraining the safe development of the industry. Existing prediction methods mostly adopt unified models while neglecting the impact of individual pilot capability differences on error patterns. This study proposes a real-time prediction framework for UAV pilot operational errors that integrates multidimensional adaptability features. Through a dual-channel network architecture, it separately extracts operational temporal features and pilot adaptability features, utilizes a pilot-conditional attention mechanism to inject static adaptability profiles as biases into temporal predictions, and designs an adaptive threshold optimization algorithm based on adaptability weighting to dynamically adjust warning parameters according to pilot capability levels. Based on large-scale measured data from 665 pilots, including seven adaptability indicators—finger dexterity, control function, depth perception, dynamic visual acuity, speed estimation, motion stability, and complex reaction—experimental results demonstrate that the fusion model achieves 89.7% accuracy, an AUC value of 0.928, and an average warning time of 3.2 s, significantly outperforming traditional methods. Complex reaction, control function, and motion stability contribute most to error prediction, accounting for 28.3%, 25.6%, and 19.5%, respectively. The adaptive threshold strategy reduces the false negative rate of the low-adaptability group by 50.4% and the false positive rate of the high-adaptability group by 64.6%. The model maintains stable performance across five scenarios—takeoff, cruise, hover, emergency avoidance, and landing—and maintains 84.6% accuracy under 20% data missing conditions. The research outcomes provide technical support for the transformation of UAV operational safety management from post-incident analysis to proactive prevention and from unified standards to personalized adaptation, offering important guidance for pilot selection, training, and intelligent assistance system design.