Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-30T17:49:08.366Z Has data issue: false hasContentIssue false
  • English
  • Français

A prediction model of vessel trajectory based on generative adversarial network

Published online by Cambridge University Press:  26 April 2021

Senjie Wang  and
Zhengwei He
Senjie Wang
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, China.
Zhengwei He*
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, China. Key Laboratory of Hubei Province for Inland Navigation Technology, Wuhan University of Technology, Wuhan, China. National Engineering Research Center for Water Transportation Safety, Wuhan University of Technology, Wuhan, China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

trajectory predictionship encountergenerative adversarial networkdeep learning
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 5 , September 2021 , pp. 1161 - 1171
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bahdanau, D., Cho, K. and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.Google Scholar
Borkowski, P. (2010). Computational mathematics in marine navigation. Zeszyty Naukowe/Akademia Morska w Szczecinie, 21, 20–27.Google Scholar
Borkowski, P. (2017). The ship movement trajectory prediction algorithm using navigational data fusion. Sensors, 17, 1432.CrossRefGoogle ScholarPubMed
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 3, 2672–2680.Google Scholar
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S. and Alahi, A. (2018). Social gan: Socially Acceptable Trajectories with Generative Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2255–2264.CrossRefGoogle Scholar
Huang, H. (2018). Trajectory Prediction for Ocean Vessels Base on K-Order Multivariate Markov Chain. Proceedings of the Wireless Algorithms, Systems, and Applications: 13th International Conference, WASA 2018, Tianjin, China, June 20–22, 2018, 140.Google Scholar
Kaluza, P., Kölzsch, A., Gastner, M. T. and Blasius, B. (2010). The complex network of global cargo ship movements. Journal of the Royal Society Interface, 7, 1093–1103.CrossRefGoogle ScholarPubMed
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.Google Scholar
Krzysztof, J. (2017). Automatic identification system (AIS) dynamic data estimation based on discrete Kalman Filter (KF) algorithm. Scientific Journal of Polish Naval Academy, 211, 71–87.Google Scholar
Li, W., Zhang, C., Ma, J. and Jia, C. (2019). Long-term Vessel Motion Predication by Modeling Trajectory Patterns with AIS Data. 2019 5th International Conference on Transportation Information and Safety (ICTIS), 1389–1394.CrossRefGoogle Scholar
Liu, C., Guo, S., Feng, Y., Hong, F., Huang, H. and Guo, Z. (2019a). L-VTP: Long-term vessel trajectory prediction based on multi-source data analysis. Sensors, 19, 4365.CrossRefGoogle Scholar
Liu, J., Shi, G. and Zhu, K. (2019b). Vessel trajectory prediction model based on AIS sensor data and adaptive chaos differential evolution support vector regression (ACDE-SVR). Applied Sciences, 9, 2983.CrossRefGoogle Scholar
Luong, M.-T., Pham, H. and Manning, C. D. (2015). Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025.Google Scholar
Perera, L. P. and Soares, C. G. (2010). Ocean Vessel Trajectory Estimation and Prediction Based on Extended Kalman Filter. The Second International Conference on Adaptive and Self-Adaptive Systems and Applications.Google Scholar
Sainath, T. N., Vinyals, O., Senior, A. and Sak, H. C. (2015). Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 4580–4584.CrossRefGoogle Scholar
Sun, L. and Zhou, W. (2017). Vessel Motion Statistical Learning Based on Stored AIS Data and Its Application to Trajectory Prediction. 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017). Atlantis Press, 1183–1189.CrossRefGoogle Scholar
Suo, Y., Chen, W., ClaramunT, C. and Yang, S. (2020). A ship trajectory prediction framework based on a recurrent neural network. Sensors, 20, 5133.CrossRefGoogle ScholarPubMed
Sutulo, S., Moreira, L. and Soares, C. G. (2002). Mathematical models for ship path prediction in manoeuvring simulation systems. Ocean Engineering, 29, 1–19.CrossRefGoogle Scholar
Tang, H., Yin, Y. and Shen, H. (2019). A model for vessel trajectory prediction based on long short-term memory neural network. Journal of Marine Engineering & Technology, 1–10. doi: 10.1080/20464177.2019.1665258CrossRefGoogle Scholar
Tu, E., Zhang, G., Rachmawati, L., Rajabally, E. and Huang, G.-B. (2017). Exploiting AIS data for intelligent maritime navigation: A comprehensive survey from data to methodology. IEEE Transactions on Intelligent Transportation Systems, 19, 1559–1582.CrossRefGoogle Scholar
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y. and Change Loy, C. (2018). Esrgan: Enhanced Super-Resolution Generative Adversarial Networks. Proceedings of the European Conference on Computer Vision (ECCV).Google Scholar
Zhang, X., Liu, G., Hu, C. and Ma, X. (2019). Wavelet Analysis Based Hidden Markov Model for Large Ship Trajectory Prediction. 2019 Chinese Control Conference (CCC),. 2913–2918.CrossRefGoogle Scholar