Ship Autonomous Collision-Avoidance Strategies—A Comprehensive Review

Lyu, Hongguang; Hao, Zengrui; Li, Jiawei; Li, Guang; Sun, Xiaofeng; Zhang, Guoqing; Yin, Yixin; Zhao, Yanjie; Zhang, Lunping

doi:10.3390/jmse11040830

Cited by 17 publications

(5 citation statements)

References 110 publications

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“…Navigational safety depends heavily on the knowledge and experience of the OOWs, who are key in making and executing collision avoidance decisions. Therefore, some scholars, based on the COLREG, excellent maritime navigation techniques, and the practical collision avoidance experience of Officers on Watch (OOW), have constructed a knowledge base [52]. This knowledge base forms the foundation of an expert system designed to assist in ship collision avoidance decision making.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Determination of Ship Collision Avoidance Timing Using Machine Learning Method

Zhou,

Du,

Liu

et al. 2024

Sustainability

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“…To study motion prediction systematically for intelligent navigation, we provide an overview and analysis of the methods and applications of motion prediction across different time scales. In previous studies [16][17][18], motion prediction is taken only as a part of route planning, motion control, or traffic simulation individually. It is essential to consider the overall motion-prediction models, algorithms, and systems for different time scales.…”

Section: Introductionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

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“…However, the rapid growth of maritime transport has led to an increased risk of ship collisions. As reported by the International Maritime Organization (IMO), there has been an annual average of about 75 ship collisions globally in the past decade, causing significant human and financial losses, and posing threats to the marine environment [2]. Against this backdrop, the importance of ship trajectory prediction technology is increasingly evident.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Maritime Navigational Safety: Ship Trajectory Prediction Using ACoAtt–LSTM and AIS Data

Li,

et al. 2024

IJGI

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Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability.

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Ship Autonomous Collision-Avoidance Strategies—A Comprehensive Review

Cited by 17 publications

References 110 publications

Determination of Ship Collision Avoidance Timing Using Machine Learning Method

Determination of Ship Collision Avoidance Timing Using Machine Learning Method

A Review on Motion Prediction for Intelligent Ship Navigation

Enhancing Maritime Navigational Safety: Ship Trajectory Prediction Using ACoAtt–LSTM and AIS Data

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