A High-Frequency Composite Safety Index for Real-Time Risk Assessment of Maritime Autonomous Surface Ships Using Full-Scale Voyage Data

Maritime Autonomous Surface Ships (MASSs) require a safety regime that expresses the state of a vessel continuously rather than monitoring each hazard in isolation. No such measure is currently in operational use, and the reason is not that the wrong way of combining hazards has been chosen: a continuous, multi-domain measurement of the ship’s state has not been available. This paper proposes High-Frequency Composite Safety Index (HF-CSI) computed from operational data already generated onboard. Weather, collision, machinery health, and grounding risk are normalized onto a common 0–10 scale using externally referenced thresholds so that a given value carries the same meaning across voyages and vessels, and they are combined through a non-compensatory weighted-maximum rule that prevents a critical hazard in one domain from being masked by favorable conditions elsewhere. The domain responsible for the value is reported alongside it, meaning any degradation is immediately attributable to a specific domain. Machinery health assessment uses operating-mode-conditional baselines, derived from the engine telegraph signal, to eliminate false anomalies from normal propulsion-mode changes. The framework was evaluated using full-scale data from a large ocean-going container ship in commercial service over a 6.5-day voyage (1858 samples, 5 min resolution). The voyage-mean HF-CSI was 8.01 (SD 3.15), with 68.2% Normal-grade samples, consistent with an incident-free voyage. The dominant risk shifted by sea area—weather in the Bay of Biscay, grounding in the North Sea, machinery health in the English Channel—with no single domain explaining more than 65% of risk-bearing samples. Against compensatory averaging, non-compensatory fusion avoided underestimating hazards, correctly flagging a heavy-weather segment that averaging rated Normal. Sensitivity analysis across 10 conditions kept HF-CSI within 7.58–8.44. Synthetic head-on encounters generated on the recorded track confirmed that the collision component separates decisive, minimal, and absent avoidance behavior, including a case that satisfied COLREG Rule 14 yet produced a risk comparable to non-compliance.