Long-term Vessel Motion Predication by Modeling Trajectory Patterns with AIS Data

Li, Wenkai; Zhang, Chunwei; Ma, Jian; Jia, Chengfeng

doi:10.1109/ictis.2019.8883596

Cited by 14 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their work achieved an average error of predictionthat was less than 280 m in the future 10 min. Li et al (Li et al, 2019) utilised the clustering method to capture the trajectories' features firstly and then used the LSTM to achieve an average error of prediction that was less than 50 m in the future 10 min. Liu et al (2019b) improved the prediction performance of the plain Support Vector Machine (SVM) model through the adaptive chaos differential evolution algorithm to optimise the model parameters.…”

Section: Introductionmentioning

confidence: 99%

A prediction model of vessel trajectory based on generative adversarial network

Wang

2021

J. Navigation

View full text Add to dashboard Cite

Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

show abstract

Section: Introductionmentioning

confidence: 99%

A prediction model of vessel trajectory based on generative adversarial network

Wang

2021

J. Navigation

View full text Add to dashboard Cite

show abstract

“…To overcome this problem, long-term trajectory prediction approaches have been devised with the inclusion of memory features, such as the recurrent neural networks (RNNs), with their most notable representative, i.e., the long short-term memory (LSTM) NNs already used in the context of the vessel trajectory prediction problem [22][23][24][25]. Besides trajectory modeling and prediction in open waters, advances have also been made in crowded port waters as in [26], where another modification of the RNNs, namely the bidirectional gated recurrent unit, is used to address the vessel trajectory prediction problem, outperforming standard NN methods in such scenarios.…”

Section: Introductionmentioning

confidence: 99%

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions

Papadimitrakis

Stogiannos

Sarimveis

et al. 2021

Sensors

View full text Add to dashboard Cite

The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel.

show abstract

“…After that, the GPU-accelerated method has been introduced in the maritime IoT devices in the maritime industry (Huang et al, 2020). Li et al (2019) supposes that the long-term prediction is more useful than the short-term motion prediction considering the restricted manoeuvrability of vessels. It proposes the LSTM bnetwork combined the longest common sub-sequence algorithm to find the long term motion.…”

Section: Introductionmentioning

confidence: 99%

A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network

et al. 2020

View full text Add to dashboard Cite

With a wide use of AIS data in maritime transportation, there is an increasing demand to develop algorithms to efficiently classify a ship's AIS data into different movements (static, normal navigation and manoeuvring). To achieve this, several studies have been proposed to use labeled features but with the drawback of not being able to effectively extract the details of ship movement information. In addition, a ship movement is in a free space, which is different to a road vehicle's movement in road grids, making it inconvenient to directly migrate the methods for GPS data classification into AIS data. To deal with these problems, a Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) algorithm is proposed in this paper. The underlying concept of this method is to train a neural network to learn from the labeled AIS data, and the unlabeled AIS data can be effectively classified by using this trained network. More specifically, a Ship Movement Image Generation and Labelling (SMIGL) algorithm is first designed to convert a ship's AIS trajectories into different movement images to make a full use of the CNN's classification ability. Then, a CNN-SMMC architecture is built with a series of functional layers (convolutional layer, max-pooling layer, dense layer etc.) for ship movement classification with seven experiments been designed to find the optimal parameters for the CNN-SMMC. Considering the imbalanced features of AIS data, three metrics (average accuracy, 1 score and Area Under Curve (AUC)) are selected to evaluate the performance of the CNN-SMMC. Finally, several benchmark classification algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Decision Tree (DT)) are selected to compare with CNN-SMMC. The results demonstrate that the proposed CNN-SMMC has a better performance in the classification of AIS data.

show abstract

Long-term Vessel Motion Predication by Modeling Trajectory Patterns with AIS Data

Cited by 14 publications

References 11 publications

A prediction model of vessel trajectory based on generative adversarial network

A prediction model of vessel trajectory based on generative adversarial network

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions

A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network

Contact Info

Product

Resources

About