Vehicle Trajectory Prediction Method Coupled With Ego Vehicle Motion Trend Under Dual Attention Mechanism-Reference-Cited by-同舟云学术

Vehicle Trajectory Prediction Method Coupled With Ego Vehicle Motion Trend Under Dual Attention Mechanism

Published:2022 Issue: Volume:71 Page:1-16
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Guo Hongyan¹^ORCID,Meng Qingyu¹^ORCID,Cao Dongpu²^ORCID,Chen Hong³^ORCID,Liu Jun¹^ORCID,Shang Bingxu⁴^ORCID

Affiliation:

1. Department of Control Science and Engineering, State Key Laboratory of Automotive Simulation, and Control, Jilin University (Campus Nanling), Changchun, China

2. Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada

3. Clean Energy Automotive Engineering Center, Tongji University, Shanghai, China

4. Institute Intelligent Driving Department, Intelligent Connected Vehicle Development, FAW Group Corporation, Ltd., Changchun, China

Funder

National Nature Science Foundation of China

Project of the Science and Technology Department, Jilin Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09749176.pdf?arnumber=9749176

Reference53 articles.

1. HSTA: A Hierarchical Spatio-Temporal Attention Model for Trajectory Prediction

2. A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries

3. A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction

4. Temporal pattern attention for multivariate time series forecasting

5. A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting

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