Attention Mechanism-based Forward and Backward Data-Driven Method for Ship Trajectory Prediction-Reference-Cited by-同舟云学术

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Attention Mechanism-based Forward and Backward Data-Driven Method for Ship Trajectory Prediction

Published:2024-05-24 Issue: Volume: Page:1-6
ISSN:
Container-title:Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things
language:
Short-container-title:

Author:

Xiao Ye¹^ORCID,Hu Yupeng¹^ORCID,Yin Jiangjin²^ORCID,Xiao Yi³^ORCID,Jiang Hanmin⁴^ORCID,Liu Qianzhen¹^ORCID

Affiliation:

1. College of Computer Science and Electronic Engineering, Hunan University, China

2. College of Informatics, Huazhong Agricultural University, China

3. School of Electrical Engineering, North China University of Water Resources and Electric Power, China

4. School of Computer Science and Technology, Jilin University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3670105.3670106

Reference16 articles.

1. Deep Learning Methods for Vessel Trajectory Prediction Based on Recurrent Neural Networks

2. Intent inference of ship maneuvering for automatic ship collision avoidance

3. Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).

4. A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

5. L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis

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