Intent prediction of vessels in intersection waterway based on learning vessel motion patterns with early observations

Ma, Jian; Jia, Chengfeng; Shu, Yu; Liu, Kezhong; Hu, Yulong

doi:10.1016/j.oceaneng.2021.109154

Cited by 24 publications

(3 citation statements)

References 25 publications

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“…To enhance the awareness of the surroundings in intersecting waterways and avoid traffic accidents, accumulated long short-term memory (ALSTM), a recurrent neural network architecture first described by Ma et al [79], was used. The method uses skip connections and an adaptive memory module to get around the drawbacks of the traditional LSTM (Figure 21).…”

Section: Lstmmentioning

confidence: 99%

Artificial Intelligence in Ship Trajectory Prediction

Bi,

Cheng,

Zhang

et al. 2024

JMSE

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Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research.

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Section: Lstmmentioning

confidence: 99%

Artificial Intelligence in Ship Trajectory Prediction

Bi,

Cheng,

Zhang

et al. 2024

JMSE

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“…This model simulates the dynamics and movement patterns of ships across spatial and temporal dimensions, incorporating features of ship trajectories that are sensitive to time into the prediction framework. Ma et al (2021), by conducting statistical analysis on recorded ship movement trajectories, discovered that ship movements frequently show a strong correlation with their long-term historical trajectories. Consequently, they proposed an augmented long short-term memory network (ALSTM), which incorporates skip connections and adaptive memory modules into the traditional LSTM structure.…”

Section: Overview Of Related Work On Ship Behavior Miningmentioning

confidence: 99%

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Li,

Liu,

Wang

et al. 2024

Front. Mar. Sci.

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In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

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“…With this method, the raw AIS data was transformed as clean data with equal frequency, in which the frequency was 1 Hz. (This method of cleaning and interpolating the AIS data has been described in our previous work [31].) Finally, using the time index and distance from the database, a set of encountering trajectory pairs at the same time and within six nautical miles apart were selected.…”

Section: Features Extractionmentioning

confidence: 99%

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Liu

Jia

2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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Frequent collision accidents of ships in intersection waters have caused huge casualties and property losses. Unclear encounter intention, poor communication, or inaccurate judgment of the encounter intention are often the major causes of ships falling into dangerous and urgent situations, leading to collision accidents. There are few methods and models for automatically inferring ship encounter intention. In this study, an intelligent model driven by AIS data is proposed to infer the ship encounter intention in intersection waters. The Hidden Markov Model (HMM) is adopted to formulate the encounter process and perform intention inference. The encounter intentions, including crossing, overtaking and head-on, are modeled as unobservable states of the formulated HMM. The observable measures of HMM extracted from AIS data, include the relative distance, relative speed, and course difference between two ships. Subsequently, the Forward-Backward algorithm is employed to obtain the model parameters and the Viterbi algorithm is exploited to estimate the hidden state with the highest probability, resulting in the inferred intention. The main advantage of the proposed model is its ability to capture the spatial-temporal characteristics of the encounter process, that is, the spatial interaction between ships and the dynamic evolution of states of the encounter process. The AIS data collected from the Lantau Strait intersection waters are adopted to verify the effectiveness of the proposed model. The experimental results reveal that the model can achieve an inference accuracy of 95%, 91.33%, and 92.67% for crossing, overtaking, and head-on, respectively. Moreover, it has real-time performance that ensures the encounter intentions can be recognized at an early stage, which is very critical for the safe navigation of any ships encountered. Our results show that our model can infer the encounter intentions in a timely manner and with high accuracy.

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Intent prediction of vessels in intersection waterway based on learning vessel motion patterns with early observations

Cited by 24 publications

References 25 publications

Artificial Intelligence in Ship Trajectory Prediction

Artificial Intelligence in Ship Trajectory Prediction

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

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