Title Enhancing maritime safety: estimating collision probabilities with trajectory prediction boundaries using deep learning models /
Authors Jurkus, Robertas ; Venskus, Julius ; Markevičiūtė, Jurgita ; Treigys, Povilas
DOI 10.3390/s25051365
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Is Part of Sensors.. Basel : MDPI. 2025, vol. 25, iss. 5, art. no. 1365, p. [1-26].. ISSN 1424-8220. eISSN 1424-8220
Keywords [eng] collision risk score ; conformal prediction regions ; long short-term memory ; uncertainty quantification ; vessel collision detection ; vessel trajectory prediction boundaries
Abstract [eng] We investigate maritime accidents near Bornholm Island in the Baltic Sea, focusing on one of the most recent vessel collisions and a way to improve maritime safety as a prevention strategy. By leveraging Long Short-Term Memory autoencoders, a class of deep recurrent neural networks, this research demonstrates a unique approach to forecasting vessel trajectories and assessing collision risks. The proposed method integrates trajectory predictions with statistical techniques to construct probabilistic boundaries, including confidence intervals, prediction intervals, ellipsoidal prediction regions, and conformal prediction regions. The study introduces a collision risk score, which evaluates the likelihood of boundary overlaps as a metric for collision detection. These methods are applied to simulated test scenarios and a real-world case study involving the 2021 collision between the Scot Carrier and Karin Hoej cargo ships. The results demonstrate that CPR, a non-parametric approach, reliably forecasts collision risks with 95% confidence. The findings underscore the importance of integrating statistical uncertainty quantification with deep learning models to improve navigational decision-making and encourage a shift towards more proactive, AI/ML-enhanced maritime risk management protocols.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2025
CC license CC license description