Fractal–Episodic Assessment of Ship Control Microvariability for Human-Factor-Aware Navigation Risk Monitoring in Maritime Autonomous Systems

The rapid development of Maritime Autonomous Surface Ships (MASS) requires advanced data-driven approaches for navigation safety monitoring and human-factor-aware risk analysis. This research proposes a fractal–episodic framework for assessing ship-control microvariability from normalized AIS/ECDIS trajectories in risk-oriented navigation monitoring, with particular relevance to MASS. The framework converts local micro-motion irregularities into passage-level indicators through sliding-window analysis of XTE-derived signals; computation of Higuchi, DFA, and Katz fractal measures; formation of a nine-component track signature; min–max normalization; and weighted aggregation into a chaos score complemented by a confidence index. The proposed framework can support intelligent monitoring and decision-support systems in autonomous maritime operations by providing interpretable behavioral indicators derived from AIS/ECDIS data.