A prediction model of vessel trajectory based on generative adversarial network

Wang, Senjie; He, Zhengwei

doi:10.1017/s0373463321000382

Cited by 31 publications

(9 citation statements)

References 16 publications

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“…Artificial neural networks was applied to trajectory prediction using AIS data [25,33,37,38]. Chen et al [7] highlighted the noise issue affecting the quality of AIS data and proposed a method to predict trajectories using neural networks.…”

Section: Related Workmentioning

confidence: 99%

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Gouareb

Can²,

Ferdowsi

et al. 2022

Lecture Notes in Computer Science

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As the world's population continues to expand, maritime transport is critical to ensure economic growth. To improve security and safety of maritime transportation, the Automatic Identification System AQ1 (AIS) collects real-time data about vessels and their positions. While a large portion of the AIS data is provided via an automatic tracking system, some key fields, such as destination and draught, are entered manually by the ship navigator and are thus prone to errors. To support decision making in maritime industries, in this paper we propose a datadriven vessel destination prediction algorithm based on heterogeneous graph and machine learning models. We design the task as a multi-class classification problem, where the destination port is the category to be predicted given the vessel and origin information. Then, we use a link prediction model in a weighted heterogeneous graph to predict the vessel destination. Experimental comparison against baseline methods, such as logistic regression and k-nearest neighbors, showed that our model provides a robust performance, outperforming the baseline algorithms by 9% and 33% in terms of accuracy and F1-score, respectively. Thus, heterogeneous graph models provide a powerful alternative to predict port destination, and could support enhancing AIS data quality and better decision making in maritime transportation industries.

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Section: Related Workmentioning

confidence: 99%

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Gouareb

Can²,

Ferdowsi

et al. 2022

Lecture Notes in Computer Science

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“…The research was tested on the historical track data of Zhoushan Port. The results showed that compared with seq2seq, GAN and Kalman models, the prediction accuracy of GAN-AI model was improved by 20%, 24% and 72% respectively [10]. Li et al proposed a lane keeping algorithm based on scene analysis to ensure the driver's comfort when driving state changes.…”

Section: Related Workmentioning

confidence: 99%

“…In formula (9), AMX  is the maximum eigenvalue of SN . The consistency test of single level and total level is carried out in MATLAB, and the corresponding weights of environmental factors can be obtained as shown in formula (10).…”

Section: Design Of Intelligent Navigation Obstacle Avoidance Algorithmmentioning

confidence: 99%

Application of Intelligent Obstacle Avoidance Algorithm Combined with Internet of Things Technology in Navigation

Guo

Wang²

2023

Preprint

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With the prosperity and development of the Maritime Silk Road, China's maritime industry has reached a new height. While the maritime transport industry has been vigorously developed, it has also brought great challenges to safe navigation. To realize intelligent navigation, effectively prevent maritime collision accidents, and improve navigation safety, a structural model of intelligent navigation obstacle avoidance platform based on Internet of Things technology is first proposed. Then the research combines the analytic hierarchy process, artificial neural network and BP neural network algorithm, and introduces environmental factors to design an optimized intelligent navigation obstacle avoidance algorithm, so that the algorithm can make real-time intelligent adjustment strategies according to the changes of the actual environment. Finally, the collision risk at the location of the research ship is judged, and the priority list of obstacle avoidance is constructed by the risk value between different ships and the research ship, providing reference for the pilot. The research results show that the prediction accuracy of I-INOA algorithm is 97.83%. In the two obstacle avoidance experiments, the decision-making efficiency of the four ships based on I-IONA algorithm is the highest, which is 1. In practical application, the priority list of obstacle avoidance is P, O and S2. In conclusion, I-INOA algorithm has better performance and practicability, enabling the research ship to respond more intelligently and quickly.

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“…However, based on the supervised method, the cost of manual annotation is very high and adaptability to new scenarios is not sufficient and must be improved. For this problem, methods based on weak supervised or unsupervised learning [50] are considered, such as the use of generative adversarial network (GAN) to augment the maritime dataset [51].…”

Section: Limitationsmentioning

confidence: 99%

Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment

Shao

Lyu

Yin

et al. 2022

JMSE

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Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet* that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model.

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A prediction model of vessel trajectory based on generative adversarial network

Cited by 31 publications

References 16 publications

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Application of Intelligent Obstacle Avoidance Algorithm Combined with Internet of Things Technology in Navigation

Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment

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