TrAISformer-A generative transformer for AIS trajectory prediction

Nguyen, D.; Fablet, Ronan

doi:10.48550/arxiv.2109.03958

Cited by 13 publications

(18 citation statements)

References 34 publications

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“…The evaluation metric for the experiment is defined as follows: The prediction error at time steps was measured using the Haversine distance [26] between the true position and the predicted position. The Haversine distance [26], a mathematical formula that calculates the great-circle distance between two points on the Earth's surface, is a particularly useful metric for evaluating the accuracy of models in long-term prediction tasks.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Zhao,

Wang,

Gao

et al. 2023

JMSE

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Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to limitations in underwater communications, posing challenges for the execution of underwater exploration missions. Therefore, trajectory prediction based on AIS data is vital in the fusion of underwater detection information. However, traditional models for underwater vessel trajectory prediction typically work well for only small-scale and short-term predictions. In this paper, a novel deep learning method is proposed that leverages a look-back window to decompose the temporal and motion features of ship movement trajectories, enabling long-term vessel prediction in broader sea areas. This research introduces an innovative model structure that enables trajectory features to be simultaneously learned for a larger range of vessels and facilitates long-term prediction. Through this innovative model design, the proposed model can more accurately predict vessel trajectories, providing reliable and comprehensive forecasting results. Our proposed model outperforms the Nlinear model by a 16% improvement in short-term prediction accuracy and an approximately 8% improvement in long-term prediction accuracy. The model also outperforms the Patch model by 5% in accuracy. In summary, the proposed method can produce competitive predictions for the long-term future trajectory trends of ships in large-scale sea areas.

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Section: Evaluation Criteriamentioning

confidence: 99%

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Zhao,

Wang,

Gao

et al. 2023

JMSE

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“…With the attention-based model mechanism being widely used in image and natural language processing tasks, the multi-head attention (MHA) mechanism emerges as the situation requires [32]. An MHA is a combination of multiple self-attention structures.…”

Section: Application Of Mha Mechanismmentioning

confidence: 99%

An Improved Ship Trajectory Prediction Based on AIS Data Using MHA-BiGRU

Bao

Gao

et al. 2022

JMSE

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According to the statistics of water transportation accidents, collision accidents are on the rise as the shipping industry has expanded by leaps and bounds, and the water transportation environment has become more complex, which can result in grave consequences, such as casualties, environmental destruction, and even massive financial losses. In view of this situation, high-precision and real-time ship trajectory prediction based on AIS data can serve as a crucial foundation for vessel traffic services and ship navigation to prevent collision accidents. Thus, this paper proposes a high-precision ship track prediction model based on a combination of a multi-head attention mechanism and bidirectional gate recurrent unit (MHA-BiGRU) to fully exploit the valuable information contained in massive AIS data and address the insufficiencies in existing trajectory prediction methods. The primary advantages of this model are that it allows for the retention of long-term ship track sequence information, filters and modifies ship track historical data for enhanced time series prediction, and models the potential association between historical and future ship trajectory status information with the current state via the bidirectional gate recurrent unit. Significantly, the introduction of a multi-head attention mechanism calculates the correlation between the characteristics of AIS data, actively learns cross-time synchronization between the hidden layers of ship track sequences, and assigns different weights to the result based on the input criterion, thereby enhancing the accuracy of forecasts. The comparative experimental results also verify that MHA-BiGRU outperforms the other ship track prediction models, demonstrating that it possesses the characteristics of ease of implementation, high precision, and high reliability.

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“…Machine learning methods involve Gaussian process regression models, 24 support vector machines, 25 and k-nearest neighbours. 26 Finally, examples of deep learning methods are convolutional neural networks, 27 LSTM, 28 bidirectional Gated Recurrent Units (GRU) 29 and transformer 30,31 algorithms. The latter can train historical AIS data to capture limited ship motion features and predict ship motion trajectories in advance.…”

Section: Introductionmentioning

confidence: 99%

A deep learning method for the prediction of 6-DoF ship motions in real conditions

Zhang

Taimuri

Xin

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part M:

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This paper presents a deep learning method for the prediction of ship motions in 6 Degrees of Freedom (DoF). Big data streams of Automatic Identification System (AIS), now-cast, and bathymetry records are used to extract motion trajectories and idealise environmental conditions. A rapid Fluid-Structure Interaction (FSI) model is used to generate ship motions that account for the influence of surrounding water and ship-controlling devices. A transformer neural network that accounts for the influence of operational conditions on ship dynamics is validated by learning the data streams corresponding to ship voyages and hydro-meteorological conditions between two ports in the Gulf of Finland. Predictions for a ship turning circle and motion dynamics between these two ports show that the proposed method can capture the influence of operational conditions on seakeeping and manoeuvring.

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TrAISformer-A generative transformer for AIS trajectory prediction

Cited by 13 publications

References 34 publications

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

An Improved Ship Trajectory Prediction Based on AIS Data Using MHA-BiGRU

A deep learning method for the prediction of 6-DoF ship motions in real conditions

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