Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm

Xiaopeng Gao; Xiaohong Liu; Mingjin Xu

doi:10.1007/978-981-95-7664-7_16

Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm

Xiaopeng Gao
, Xiaohong Liu
, Mingjin Xu^*

^*此作品的通讯作者

Naval University of Engineering Wuhan

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Unmanned Surface Vehicles (USVs) are widely used in marine exploration, security, and autonomous navigation, where trajectory prediction plays a critical role in decision-making. This study proposes a prediction framework that combines a high-fidelity ship dynamics model with a Soft Actor-Critic (SAC) algorithm to optimize a Long Short-Term Memory (LSTM) network. The dynamics model includes first-order response delay and stochastic wind-wave disturbances to simulate realistic data. SAC adaptively tunes key LSTM hyperparameters, improving optimization efficiency and enhancing generalization under complex sea conditions. Experimental results show that the SAC-optimized LSTM outperforms manually tuned models, highlighting the potential of integrating deep reinforcement learning with sequence modeling for USV navigation.

源语言	英语
主期刊名	Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 4
编辑	Shaorong Xie, Yifeng Niu, Wenxing Fu, Yi Qu
出版商	Springer Science and Business Media Deutschland GmbH
页	169-182
页数	14
ISBN（印刷版）	9789819576630
DOI	https://doi.org/10.1007/978-981-95-7664-7_16
出版状态	已出版 - 2026
已对外发布	是
活动	5th International Conference on Autonomous Unmanned Systems, ICAUS 2025 - Shanghai, 中国期限: 17 10月 2025 → 19 10月 2025

出版系列

姓名	Lecture Notes in Electrical Engineering
卷	1577 LNEE
ISSN（印刷版）	1876-1100
ISSN（电子版）	1876-1119

会议

会议	5th International Conference on Autonomous Unmanned Systems, ICAUS 2025
国家/地区	中国
市	Shanghai
时期	17/10/25 → 19/10/25

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 14 水下生物

访问文件

10.1007/978-981-95-7664-7_16

其它文件与链接

链接到 Scopus 的出版物

引用此

Gao, X., Liu, X., & Xu, M. (2026). Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm. 在 S. Xie, Y. Niu, W. Fu, & Y. Qu (编辑), Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 4 (页码 169-182). (Lecture Notes in Electrical Engineering; 卷 1577 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-95-7664-7_16

Gao, Xiaopeng ; Liu, Xiaohong ; Xu, Mingjin. / Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm. Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 4. 编辑 / Shaorong Xie ; Yifeng Niu ; Wenxing Fu ; Yi Qu. Springer Science and Business Media Deutschland GmbH, 2026. 页码 169-182 (Lecture Notes in Electrical Engineering).

@inproceedings{5e89993643484472976b9b0e22a3323b,

title = "Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm",

abstract = "Unmanned Surface Vehicles (USVs) are widely used in marine exploration, security, and autonomous navigation, where trajectory prediction plays a critical role in decision-making. This study proposes a prediction framework that combines a high-fidelity ship dynamics model with a Soft Actor-Critic (SAC) algorithm to optimize a Long Short-Term Memory (LSTM) network. The dynamics model includes first-order response delay and stochastic wind-wave disturbances to simulate realistic data. SAC adaptively tunes key LSTM hyperparameters, improving optimization efficiency and enhancing generalization under complex sea conditions. Experimental results show that the SAC-optimized LSTM outperforms manually tuned models, highlighting the potential of integrating deep reinforcement learning with sequence modeling for USV navigation.",

keywords = "High-Fidelity Modeling, LSTM, SAC, Trajectory Prediction, USV",

author = "Xiaopeng Gao and Xiaohong Liu and Mingjin Xu",

note = "Publisher Copyright: {\textcopyright} Beijing HIWING Scientific and Technological Information Institute 2026.; 5th International Conference on Autonomous Unmanned Systems, ICAUS 2025 ; Conference date: 17-10-2025 Through 19-10-2025",

year = "2026",

doi = "10.1007/978-981-95-7664-7\_16",

language = "英语",

isbn = "9789819576630",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "169--182",

editor = "Shaorong Xie and Yifeng Niu and Wenxing Fu and Yi Qu",

booktitle = "Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 4",

address = "德国",

}

Gao, X, Liu, X & Xu, M 2026, Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm. 在 S Xie, Y Niu, W Fu & Y Qu (编辑), Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 4. Lecture Notes in Electrical Engineering, 卷 1577 LNEE, Springer Science and Business Media Deutschland GmbH, 页码 169-182, 5th International Conference on Autonomous Unmanned Systems, ICAUS 2025, Shanghai, 中国, 17/10/25. https://doi.org/10.1007/978-981-95-7664-7_16

Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm. / Gao, Xiaopeng; Liu, Xiaohong; Xu, Mingjin.
Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 4. 编辑 / Shaorong Xie; Yifeng Niu; Wenxing Fu; Yi Qu. Springer Science and Business Media Deutschland GmbH, 2026. 页码 169-182 (Lecture Notes in Electrical Engineering; 卷 1577 LNEE).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm

AU - Gao, Xiaopeng

AU - Liu, Xiaohong

AU - Xu, Mingjin

N1 - Publisher Copyright: © Beijing HIWING Scientific and Technological Information Institute 2026.

PY - 2026

Y1 - 2026

N2 - Unmanned Surface Vehicles (USVs) are widely used in marine exploration, security, and autonomous navigation, where trajectory prediction plays a critical role in decision-making. This study proposes a prediction framework that combines a high-fidelity ship dynamics model with a Soft Actor-Critic (SAC) algorithm to optimize a Long Short-Term Memory (LSTM) network. The dynamics model includes first-order response delay and stochastic wind-wave disturbances to simulate realistic data. SAC adaptively tunes key LSTM hyperparameters, improving optimization efficiency and enhancing generalization under complex sea conditions. Experimental results show that the SAC-optimized LSTM outperforms manually tuned models, highlighting the potential of integrating deep reinforcement learning with sequence modeling for USV navigation.

AB - Unmanned Surface Vehicles (USVs) are widely used in marine exploration, security, and autonomous navigation, where trajectory prediction plays a critical role in decision-making. This study proposes a prediction framework that combines a high-fidelity ship dynamics model with a Soft Actor-Critic (SAC) algorithm to optimize a Long Short-Term Memory (LSTM) network. The dynamics model includes first-order response delay and stochastic wind-wave disturbances to simulate realistic data. SAC adaptively tunes key LSTM hyperparameters, improving optimization efficiency and enhancing generalization under complex sea conditions. Experimental results show that the SAC-optimized LSTM outperforms manually tuned models, highlighting the potential of integrating deep reinforcement learning with sequence modeling for USV navigation.

KW - High-Fidelity Modeling

KW - LSTM

KW - SAC

KW - Trajectory Prediction

KW - USV

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DO - 10.1007/978-981-95-7664-7_16

M3 - 会议稿件

AN - SCOPUS:105041348458

SN - 9789819576630

T3 - Lecture Notes in Electrical Engineering

SP - 169

EP - 182

BT - Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 4

A2 - Xie, Shaorong

A2 - Niu, Yifeng

A2 - Fu, Wenxing

A2 - Qu, Yi

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Conference on Autonomous Unmanned Systems, ICAUS 2025

Y2 - 17 October 2025 through 19 October 2025

ER -

Gao X, Liu X, Xu M. Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm. 在 Xie S, Niu Y, Fu W, Qu Y, 编辑, Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 4. Springer Science and Business Media Deutschland GmbH. 2026. 页码 169-182. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-95-7664-7_16

Research on Unmanned Surface Vehicle Trajectory Prediction Based on the SAC-LSTM Algorithm

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

学术指纹

引用此