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In recent years, the global upswing in vessel-bridge collisions underscores the vital need for robust vessel track identification in accident prevention. Contemporary vessel trajectory identification strategies often integrate target detection with trajectory tracking algorithms, employing models like YOLO integrated with DeepSORT or Bytetrack algorithms. However, the accuracy of these methods relies on target detection outcomes and the imprecise boundary acquisition method results in erroneous vessel trajectory identification and tracking, leading to both false positives and missed detections. This paper introduces a novel vessel trajectory identification framework. The Co-tracker, a long-term sequence multi-feature-point tracking method, accurately tracks vessel trajectories by statistically calculating the translation and heading angle transformation of feature point clusters, mitigating the impact of inaccurate vessel target detection. Subsequently, vessel trajectories are predicted using a combination of Long Short-Term Memory (LSTM) and a Graph Attention Neural Network (GAT) to facilitate anomaly vessel trajectory warnings, ensuring precise predictions for vessel groups. Compared to prevalent algorithms like YOLO integrated with DeepSORT, our proposed method exhibits superior accuracy and captures crucial heading angle features. Importantly, it effectively mitigates the common issues of false positives and false negatives in detection and tracking tasks. Applied in the Three Rivers area of Ningbo, this research provides real-time vessel group trajectories and trajectory predictions. When the predicted trajectory suggests potential entry into a restricted zone, the system issues timely audiovisual warnings, enhancing real-time alert functionality. This framework markedly improves vessel traffic management efficiency, diminishes collision risks, and ensures secure navigation in multi-target and wide-area vessel scenarios. [ABSTRACT FROM AUTHOR]

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Recent developments in data collection technology and sensor precision have led to the generation of large amounts of high-quality data. The vast vessel trajectory data obtained from precise automatic identification system data facilitate the development of marine-related research fields. In particular, vessel trajectory prediction, such as preventing risks in advance or providing efficient routes by predicting the vessel location, is one of the essential parts of advanced vessel traffic service. In this study, the vessel trajectory was accurately and robustly predicted using a novel loss function. In previous studies, the loss function was designed to minimize the distance between the destination and predicted location of vessels, whereas the proposed loss function was designed to minimize the area of the triangle formed by the origin, destination, and predicted location. In experiments, the proposed approach outperformed the state-of-the-art method, reducing the mean absolute error by 12%. [ABSTRACT FROM AUTHOR]

Vessel trajectory prediction supports navigation services and collision detection. To maintain safety and efficiency in maritime transportation, vessel trajectory prediction is always an important topic. By using automatic identification system (AIS) data and deep learning methods, the task of vessel trajectory prediction has made significant progress. However, this task is still full of challenges due to the complexity of historical information dependencies and the strong influence of spatial correlations. In this paper, we introduce a novel deep learning model, PESO, based on the structure of Seq2Seq, consisting of Parallel Encoders and a Ship-Oriented Decoder. The Parallel Encoders, including the Location Encoder and the Sailing Status Encoder are designed to integrate more information into feature representation. The Ship-Oriented Decoder is targeted to utilize the Semantic Location Vector (SLV) to guide the prediction, which better represents the spatial correlation of historical track points. In order to verify the efficiency and efficacy of PESO, we conducted comparative experiments with several baseline models. The experimental results demonstrate that PESO is superior to them both quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Predicting vessel trajectories is crucial for enhancing situational awareness and preventing collisions at sea. However, achieving accurate and efficient predictions is challenging due to the heterogeneity in vessel movement patterns and changes in vessel mobility modes during voyages. To address this, we propose a new approach that uses historical AIS data to cluster route patterns for each vessel type, thereby improving prediction accuracy. By training machine learning algorithms to focus only on similar vessel types, this approach can better predict individual vessel mobility patterns. This approach offers computational advantages by using a relatively small set of trajectories from the nearest cluster of a selected vessel. Both spatial and course attributes are considered to determine the nearest cluster, while engineered features capture changes in vessel mobility modes. Using an AIS dataset from UTM Zone 10N (US West Coast), we achieved distance errors of 370 m , 742 m , and 1. 2 k m for horizons 10, 20, and 30 min, respectively, using the Random Forest algorithm for short-term trajectory prediction (≤ 30 min) with the last 1-hour trajectory of selected vessels as input. • Proposed framework for predicting the short-term future trajectory of vessels. • Enhanced prediction accuracy by addressing heterogeneity in mobility patterns. • Engineered features are incorporated to consider changes in mobility modes. • Spatial and course attributes are used for trajectory classification. • Evaluation shows high prediction accuracy with low computational cost. [ABSTRACT FROM AUTHOR]

Each vessel has its own way of sailing in the port region. Any autonomous vessel navigating such a scene should be able to predict the trajectories of surrounding ships and adjust its behaviour to avoid a collision. In this paper, combined with the sequence prediction method, a Long Short-Term Memory (LSTM) model is proposed to predict the trajectories of the vessels. The ground-truth Automatic Identification System (AIS) data in the port of Tianjin, China are used to train and test the proposed model. The experimental results prove that our model can predict ship trajectories accurately, and it is applicable to the autonomous navigation system. [ABSTRACT FROM AUTHOR]

Data-driven methods open up unprecedented possibilities for maritime surveillance using automatic identification system (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder–decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines long short-term memory RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority (DMA) show the effectiveness of deep learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the multilayer perceptron architecture. The comparative evaluation of results shows: first, the superiority of attention pooling over static pooling for the specific application, and second, the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS. [ABSTRACT FROM AUTHOR]

Vessel trajectory prediction is a critical aspect of ensuring maritime traffic safety and avoiding collisions. The long short-term memory (LSTM) network and its extensions have represented powerful ability of vessel trajectory prediction. However, the previous studies often did not take dynamic interactions between neighboring vessels into account. Additionally, in complex traffic conditions, trajectory prediction will acquire uncertainty, and these potential negative factors can limit the prediction of future trajectory. To enhance the prediction performance, we propose an interactive vessel trajectory prediction framework (i.e., QSD-LSTM) based on LSTM, which is embedded with the quaternion ship domain (QSD). The QSD is beneficial for avoiding unwanted collision between neighboring vessels. In addition, the operation of trajectory clustering is further incorporated into our trajectory prediction framework, potentially leading to more robust prediction results. Numerous experiments have been implemented on realistic automatic identification system (AIS)-based vessel trajectories to compare our QSD-LSTM with several state-of-the-art prediction methods. The prediction results have demonstrated the superior performance of our method in terms of both quantitative and qualitative evaluations. [ABSTRACT FROM AUTHOR]

Vessel trajectory prediction plays a vital role in maintaining a safe and effective status in maritime transportation. The development of deep learning provides appropriate mechanisms to predict vessel trajectory using Automatic Identification System (AIS) data. However, besides limited historical position information, much information can be used to enhance deep models, including speed, course, departure time, sailing distance and vessel type. Realistically speaking, these are related to the ships' endurance, oil storage, tide and work–rest habits. In order to address these problems, we propose a novel vessel trajectory prediction model METO-S2S based on a sequence-to-sequence structure, consisting of the Multiple-semantic Encoder and the Type-Oriented Decoder. After comprehensive data preprocessing and feature engineering, the Multiple-semantic Encoder embeds the sequential inputs of the voyage information into higher-dimensional latent vectors. In addition, the combined vector of vessel type and departure time of a particular ship is fed into the Type-Oriented Decoder treated as guidance information. To verify the efficiency and efficacy of METO-S2S, we employ an AIS dataset in US coastal waters which is more suitable for deep learning models, and implement comparative experiments with several baseline models. The experimental results show that METO-S2S is superior to them both quantitatively and qualitatively. 1 1 The code and data of our experiments is available on GitHub https://github.com/AIR-SkyForecast/METO-S2S. • The paper releases a toolkit for the AIS data preprocessing and a processed AIS dataset of US coastal waters which is more suitable for deep learning methods. • The paper develops a novel long-term vessel trajectory prediction method METO-S2S based on a Seq2Seq structure which makes good use of track information and diversified sailing features. • The paper proposes a Multiple-semantic Encoder that combines coordinate, speed, course, and sailing distance characteristics. Furthermore, the paper also proposes a Type-Oriented Decoder taking the combined vectors of vessel type and departure time as guidance information. • METO-S2S is evaluated on our released trajectory dataset. Compared with other baselines, METO-S2S can generate more accurate and robust prediction results. [ABSTRACT FROM AUTHOR]