Research on Dynamic Quaternion Ship Domain Model in Open Water Based on AIS Data and Navigator State

Liu, Dongqin; Zheng, Zhongyi; Liu, Zihao

doi:10.3390/jmse12030516

Cited by 2 publications

(1 citation statement)

References 16 publications

(35 reference statements)

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“…The aim is to provide decision support for ship operations and enhance maritime safety. Dongqin Liu et al [18] proposed a Dynamic Quaternion Ship Domain (DQSD) model based on AIS data and the navigator's state, which dynamically captures changes in the shape and size of the ship domain by considering factors such as ship maneuverability, the physical and mental states of the navigator, and environmental conditions. Silveira [19] improved the quaternion ship domain model by allowing each quadrant to have a different shape, aligning better with empirical data for cargo ships and tankers of various lengths.…”

Section: Ship Domain-based Approachmentioning

confidence: 99%

Determination of Ship Collision Avoidance Timing Using Machine Learning Method

Zhou,

Du,

Liu

et al. 2024

Sustainability

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The accurate timing for collision avoidance actions is crucial for preventing maritime collisions. Traditional methods often rely on collision risk assessments, using quantitative indicators like the Distance to the Closest Point of Approach (DCPA) and the Time to the Closest Point of Approach (TCPA). Ship Officers on Watch (OOWs) are required to execute avoidance maneuvers once these indicators reach or exceed preset safety thresholds. However, the effectiveness of these indicators is limited by uncertainties in the maritime environment and the human behaviors of OOWs. To address these limitations, this study introduces a machine learning method to learn collision avoidance behavior from empirical data of ship collision avoidance, particularly in cross-encounter situations. The research utilizes Automatic Identification System (AIS) data from the open waters around Ningbo Zhoushan Port. After data preprocessing and applying spatio-temporal constraints, this study identifies ship trajectory pairs in crossing scenarios and calculates their relative motion parameters. The Douglas–Peucker algorithm is used to identify the timing of ship collision avoidance actions and a collision avoidance decision dataset is constructed. The Random Forest algorithm was then used to analyze the factors affecting the timing of collision avoidance, and six key factors were identified: the distance, relative speed, relative bearing, DCPA, TCPA, and the ratio of the lengths of the giving-way and stand-on ships. These factors serve as inputs for the XGBoost algorithm model, which is enhanced with Particle Swarm Optimization (PSO), and thus constructing a ship collision avoidance decision model. In addition, considering the inherent errors in any model and the dynamic nature of the ship collision avoidance process, an action time window for collision avoidance is introduced, which provides a more flexible time range for ships to make timely collision avoidance responses based on actual conditions and the specific encounter environment. This model provides OOWs with accurate timing for taking collision avoidance decisions. Case studies have validated the practicality and effectiveness of this model, offering new theoretical foundations and practical guidance for maritime collision avoidance.

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Section: Ship Domain-based Approachmentioning

confidence: 99%

Determination of Ship Collision Avoidance Timing Using Machine Learning Method

Zhou,

Du,

Liu

et al. 2024

Sustainability

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Vessel Trajectory Prediction at Inner Harbor based on Deep Learning using AIS Data

Shin,

Yang

2024

Preprint

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This study proposes a novel approach for predicting vessel trajectories in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep learning techniques. Linear interpolation was applied to address unequal time intervals and limited data. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU) models were trained and evaluated, with LSTM achieving the best performance. The study also identified the critical prediction area for Vessel Traffic Service Operator (VTSO). The proposed method can contribute to enhancing safety and efficiency of vessel traffic management in complex port environments.

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Research on Dynamic Quaternion Ship Domain Model in Open Water Based on AIS Data and Navigator State

Cited by 2 publications

References 16 publications

Determination of Ship Collision Avoidance Timing Using Machine Learning Method

Determination of Ship Collision Avoidance Timing Using Machine Learning Method

Vessel Trajectory Prediction at Inner Harbor based on Deep Learning using AIS Data

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