Vessel trajectory prediction in curving channel of inland river

Xiaopeng, Tong; Chen, Xu; Sang, Lingzhi; Mao, Zhe; Wu, Qing

doi:10.1109/ictis.2015.7232156

Cited by 24 publications

(9 citation statements)

References 7 publications

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“…Establishing kinematic equation based on ship motion model is a traditional method for ship trajectory prediction, such as Markov model 1,2 , but it is difficult to establish accurate ship motion model due to the complex water environment. However, the effectiveness of statistical methods 3 depends on the validity of statistical models based on given ship trajectory data.…”

Section: Related Workmentioning

confidence: 99%

Ship trajectory prediction model based on GAN and attention mechanism

Qin¹,

Shi²

2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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In order to improve the accuracy of ship trajectory prediction in waters with complex traffic conditions such as inland rivers and ports, and solve the limitation of a single LSTM in extracting time series feature information, a ship trajectory prediction model based on generative adversarial network and attention mechanism (AGAN) is proposed. The ship's trajectory is predicted collaboratively, the ability of the model to extract key information in the trajectory is improved through the attention mechanism, the relative motion information between multiple ships is extracted through the pooling layer, the individual information and the global information are fused, and finally the generative adversarial network (GAN) is used. Features that are continuously optimized in adversarial improve the accuracy of the model. The final experimental results show that the ship trajectory prediction model based on generative adversarial network has higher accuracy.

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Section: Related Workmentioning

confidence: 99%

Ship trajectory prediction model based on GAN and attention mechanism

Qin¹,

Shi²

2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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“…e Markov model is a statistical model that can predict the trend of data changes at equal time intervals in the future based on historical data. Tong et al [25] used Markov chainand gray prediction-related methods to propose a hidden Markov model based on the adaptive update parameters of environmental data captured by dynamic objects. It showed high accuracy in curve prediction.…”

Section: Trajectory Prediction Based On Statistical Modelsmentioning

confidence: 99%

Improved Vessel Trajectory Prediction Model Based on Stacked-BiGRUs

Zhang

Ren

et al. 2022

Security and Communication Networks

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An intelligent maritime navigation system is expected to play an important role in the realm of Internet of Vessels (IoV). As a key technology in navigation systems, vessel trajectory prediction technology is critical to the IoV. Automatic identification system (AIS), an automated tracking system, is used extensively for vessel trajectory prediction. However, certain characteristics in the AIS data, such as the large number of anchored trajectories in the area, anomalous sharp turns of some trajectories, and the behavioral differences of vessels in different segments, limit the prediction accuracy. In this study, we propose a novel vessel trajectory prediction model for accurate prediction with the following characteristics: (1) an anchor trajectory elimination algorithm to eliminate anchor trajectories; (2) a statistical trajectory restoration algorithm to repair sharp turning; (3) a two-stage clustering algorithm (D-KMEANS) to distinguish vessel behavior; and (4) a deep bidirectional gate recurrent unit (Stacked-BiGRUs) model to predict vessel trajectory and compare the accuracy of the model before and after improvement. The results show that the mean square error and the mean absolute error of the improved model are reduced by 27% and 46%, respectively. This research shows good potential for maritime navigation early warning and safety.

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“…Traditional mathematical-statistical methods include the Naive Bayes algorithm, Gaussian process, Markov model, and Gray model, which are currently widely used in the trajectory prediction. Tong et al [4] improved the traditional Markov model and proposed a ship trajectory prediction method for a curved route based on the Markov chain and Gray model. Cheng et al [5] comprehensively considered the continuity of a trajectory in time and space and predicted such a trajectory using the improved Markov model, with a prediction accuracy superior than its traditional counterpart.…”

Section: B Previous Researchmentioning

confidence: 99%

Online Multiple Outputs Least-Squares Support Vector Regression Model of Ship Trajectory Prediction Based on Automatic Information System Data and Selection Mechanism

2020

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Existing maritime trajectory prediction models are faced with problems of low accuracy and inability to predict ship tracks in real time. To solve the above problem, an online multiple outputs Least-Squares Support Vector Regression model based on selection mechanism was proposed: (a) converting the traditional Least-Squares Support Vector Regression's single output to multiple outputs, aiming at the problem that the single-output of the traditional Least-Squares Support Vector Regression model is difficult to apply to complex multiple features prediction scenarios, (b) reducing the high computational complexity of matrix inversion calculations using an iterative solution, in order to solve the problem of poor real-time performance, (c) determining whether to use online model based on the characteristics of different trajectories, and (d) removing initial samples least affecting the model to alleviate the impact of large increases in the number of new samples on computational complexity. The model was simulated using the automatic identification system tracks of Tianjin port in March 2015. The calculation accuracy and efficiency of this model was verified by comparing the predicted results of the proposed model with the recurrent neural network-long short-term memory, back propagation neural network, and traditional Least-Squares Support Vector Regression models. In sum, the proposed model is highly accurate in online and real-time prediction of a target ship's trajectory when sailing at sea. In particular, it can sustain high prediction accuracy in the case of smaller data samples. The real-time predicted trajectory can assist the generation of ship collision avoidance decision-making.

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Vessel trajectory prediction in curving channel of inland river

Cited by 24 publications

References 7 publications

Ship trajectory prediction model based on GAN and attention mechanism

Ship trajectory prediction model based on GAN and attention mechanism

Improved Vessel Trajectory Prediction Model Based on Stacked-BiGRUs

Online Multiple Outputs Least-Squares Support Vector Regression Model of Ship Trajectory Prediction Based on Automatic Information System Data and Selection Mechanism

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