Trajectory Prediction and Intention Identification of Ships in Confluence Waters

WANG Zhihao; YUAN Haiwen; LI Weina; XIAO Changshi

doi:10.3963/j.jssn.1674-4861.2022.04.011

Volume 40 Issue 4

Aug. 2022

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Article Navigation > Journal of Transport Information and Safety > 2022 > 40(4): 101-109

WANG Zhihao, YUAN Haiwen, LI Weina, XIAO Changshi. Trajectory Prediction and Intention Identification of Ships in Confluence Waters[J]. Journal of Transport Information and Safety, 2022, 40(4): 101-109. doi: 10.3963/j.jssn.1674-4861.2022.04.011

Citation:

WANG Zhihao, YUAN Haiwen, LI Weina, XIAO Changshi. Trajectory Prediction and Intention Identification of Ships in Confluence Waters[J]. Journal of Transport Information and Safety, 2022, 40(4): 101-109. doi: 10.3963/j.jssn.1674-4861.2022.04.011

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Trajectory Prediction and Intention Identification of Ships in Confluence Waters

doi: 10.3963/j.jssn.1674-4861.2022.04.011

WANG Zhihao^1
,,
YUAN Haiwen^{1, 2, 3, 4
,
,},
LI Weina¹,
XIAO Changshi^{3, 4}

1.
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
2.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
3.
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
4.
Institute of Ocean Information Technology, Shandong Jiaotong University, Weihai 264200, Shandong, China

Received Date: 2022-02-24
Available Online: 2022-09-17

Abstract

Abstract

A data-driven method is proposed for predicting ship trajectory and identifying sailing intention for typical confluence waters. A CNN+LSTM combined neural network is designed by learning the historical trajectories of the ships in the confluence waters. Using CNN+LSTM as an encoder, the spatio-temporal characteristics of the navigable environment and ship sailing are extracted. The decoder, which is composed of the LSTM and full connection layer, synchronously outputs the trajectory sequence and route selection of the ship in the future period. Moreover, the Dropout layer is introduced to describe the prediction uncertainty of the proposed model. Take the same trajectory sequence as the input, multiple groups of similar prediction results are obtained by randomly disabling several neural units of the CNN+LSTM network. Based on the statistical mean and variance of the prediction results, the prediction uncertainty of ship trajectory can be estimated. A dataset is created based on the open AIS data of confluence water in the coast of the United States. The input conditions are as follows: the input time is 60 min, the sam-pling frequency is 3 min, and the dropout parameter is 0.5. The results of the proposed model show that the error of trajectory prediction is 3.946 n mile for the next 60 min. The recognition accuracy of sailing intention is 87%. And the coverage rate of uncertainty estimation is 85.7%. Compared with other LSTM-based prediction methods, the trajectory prediction error of the proposed model is reduced by 31.6% when the ship's maneuverability changes. Furthermore, the proposed CNN+LSTM model has the ability of identifying ships' sailing intentions and estimating the prediction uncertainties, which is conducive to the development of intelligent navigation and intelligent maritime supervision technology.
- traffic safety,
- confluence waters,
- trajectory prediction,
- data driven,
- identification of intention,
- uncertainty estimation

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References(23)

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