Learning Dynamic Interactions and Long-Term Patterns With Spatio-Temporal Graphs for Multi-Vessel Trajectory Prediction

Xiliang Zhang; Jin Liu; Kejie Chen; Peizhu Gong; Yuxin Liu; Zhongdai Wu

doi:10.1109/tiv.2024.3401864

ScienceGate Book Chapters

JOURNAL ARTICLE

Learning Dynamic Interactions and Long-Term Patterns With Spatio-Temporal Graphs for Multi-Vessel Trajectory Prediction

Xiliang Zhang Jin Liu Kejie Chen Peizhu Gong Yuxin Liu Zhongdai Wu

Year: 2024 Journal: IEEE Transactions on Intelligent Vehicles Vol: 9 (12)Pages: 7765-7780 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/tiv.2024.3401864

Get Full-Text PDF Get Analytical Report

Keywords:

Term (time) Trajectory Computer science Artificial intelligence Physics

Metrics

Cited By

5.61

FWCI (Field Weighted Citation Impact)

Refs

0.92

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Maritime Navigation and Safety

Physical Sciences → Engineering → Ocean Engineering

Learning Dynamic Interactions and Long-Term Patterns With Spatio-Temporal Graphs for Multi-Vessel Trajectory Prediction

Metrics

Citation History

Topics

Related Documents

A Dynamic Spatio-Temporal Refinement Network for Multi-Vessel Trajectory Prediction

Long-Short Term Spatio-Temporal Aggregation for Trajectory Prediction

Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction

Scene compliant spatio-temporal multi-modal multi-agent long-term trajectory forecasting

Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion