Collision Avoidance
Collision Avoidance on World Data Ocean: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on collision avoidance in some way — the news, the analysis, the deep dives, and the occasional surprise find. A Global Hub for Ocean Intelligence 4 World Data Ocean is a centralized digital platform where researchers, scientists, and ocean enthusiasts converge to explore, analyze, and… New stories are added to this page as we find them, so check back if you want to keep up with what is happening around collision avoidance, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything World Data Ocean is covering right now.
A ship autonomous navigation decision-making method based on the OODA loop theory
Autonomous ship navigation demands robust decision-making, particularly in complex encounter scenarios. This study introduces a novel method grounded in the OODA loop theory, establishing a closed-loop navigation logic encompassing observation, risk assessment, decision generation, and motion control. Employing a quaternion ship domain model and an improved velocity obstacle algorithm, the system generates safe, COLREGs-compliant maneuvering decisions. Simulation results demonstrate consistent safety margins, even with minimal time-to-collision. For further exploration of maritime security technologies, see our recent article on the KONGSBERG Aegir sonar family.
Adaptive collision-avoidance timing recognition for autonomous ship encounters using multi-evidence Bayesian backward evidence detection
Effective collision avoidance is paramount for maritime autonomous surface ships (MASSs). This study introduces a novel, multi-evidence Bayesian framework for adaptive collision-avoidance timing recognition, moving beyond reliance on fixed thresholds. By integrating maneuvering behavior, relative motion, and risk evolution data from historical AIS trajectories, the system learns encounter-specific trigger thresholds. Experiments involving 6,186 AIS encounters demonstrated avoidance onsets 6.50–8.60 minutes prior to peak collision risk, yielding adaptive thresholds ranging from 0.245 to 0.368, enhancing safety and operational efficiency.