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Adaptive collision-avoidance timing recognition for autonomous ship encounters using multi-evidence Bayesian backward evidence detection
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AI
DOI：10.3389/fmars.2026.1887867.png)
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Abstract
En 中文
Avoidance timing is a critical interface between collision-risk assessment and collision-avoidance planning for maritime autonomous surface ships (MASSs). Existing approaches usually trigger avoidance using fixed collision-risk thresholds; closest point of approach (CPA) criteria; ship-domain boundaries; or single maneuvering indicators; which cannot fully capture the combined effects of maneuvering behavior; relative-motion evolution; and encounter-type-dependent risk evolution. This study proposes a multi-evidence Bayesian backward evidence-detection framework to identify avoidance timing and learn encounter-specific adaptive trigger thresholds from historical automatic identification system (AIS) trajectories. Maneuvering; relative-motion; and risk-evolution evidence are integrated into a unified feature representation. Normal navigation and active avoidance are modeled as two latent states through a regularized Gaussian likelihood-ratio formulation; and avoidance onset is identified by accumulating backward evidence from the collision-risk peak. The collision-risk values at the detected avoidance-start moments are then reconstructed using weighted kernel density estimation (KDE) for overtaking; head-on; and crossing encounters; and adaptive trigger thresholds are derived from density modes with safety-advance corrections. Experiments using 6; 186 paired AIS encounter files from the Yangtze River Estuary produced 4; 361 valid avoidance-timing samples and 4; 036 effective threshold-learning samples. The detected avoidance onsets occurred 8.42; 8.60; and 6.50 min before the collision-risk peak for crossing; head-on; and overtaking encounters; respectively. The resulting adaptive thresholds were 0.245; 0.267; and 0.368. Additional train-test validation; sensitivity analysis; and multi-baseline comparison further demonstrate the stability and interpretability of the proposed framework.
Keywords:
kernel density estimation
COLREGs
maritime collision avoidance
avoidance timing
Bayesian evidence detection
collision-risk index
AIS trajectories
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