A Flexible and Explainable Vehicle Motion Prediction and Inference Framework Combining Semi-Supervised AOG and ST-LSTM-Reference-Cited by-同舟云学术

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A Flexible and Explainable Vehicle Motion Prediction and Inference Framework Combining Semi-Supervised AOG and ST-LSTM

Published:2022-02 Issue:2 Volume:23 Page:840-860
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Dai Shengzhe^ORCID,Li Zhiheng^ORCID,Li Li^ORCID,Zheng Nanning^ORCID,Wang Shuofeng^ORCID

Funder

National Key Research and Development Program of China

Science and Technology Innovation Committee of Shenzhen

Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9701814/09181448.pdf?arnumber=9181448

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4. A Hierarchical LSTM-Based Vehicle Trajectory Prediction Method Considering Interaction Information;Automotive Innovation;2024-01-04

5. A Generic Optimization-Based Enhancement Method for Trajectory Data: Two Plus One;2024

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