GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction-Reference-Cited by-同舟云学术

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GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction

Published:2024-06-02 Issue: Volume: Page:370-375
ISSN:
Container-title:2024 IEEE Intelligent Vehicles Symposium (IV)
language:
Short-container-title:

Author:

Luo Yuhao¹,Chen Kehua²,Zhu Meixin¹

Affiliation:

1. Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou),Guangzhou,China

2. Interdisciplinary Programs Office, The Hong Kong University of Science and Technology,Division of Emerging Interdisciplinary Areas (EMIA),Hong Kong,China

Funder

Guangzhou Municipal Science and Technology Project

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10587320/10588370/10588741.pdf?arnumber=10588741

Reference21 articles.

1. Real time trajectory prediction for collision risk estimation between vehicles

2. Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture

3. GRIP: Graph-based Interaction-aware Trajectory Prediction

4. DSA-GAN: Driving Style Attention Generative Adversarial Network for Vehicle Trajectory Prediction

5. Vehicles Trajectory Prediction Using Recurrent VAE Network

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