Freeway Trajectory Prediction via SpatiotemporalTransformers
Authors: 
Jingtao Peng, Ronghui Zhang, Zhifa Yang, Shengru Chen, Cong Ye, Hao Wu, Mengran LiAuthors Info & Claims
Abstract
For autonomous driving, accurate trajectory prediction is paramount, necessitating effective harnessing of spatiotemporal data. This study proposes an innovative Spatiotemporal Transformer-based model, enhancing trajectory prediction precision by leveraging a multi-head self-attention mechanism. This mechanism intricately captures both inter-vehicular interactions and temporal trajectory dependencies. The model, structured around an LSTM-based encoder-decoder framework, innovatively considers spatial interactions among observed and future trajectories during the decoding process. Evaluated on the NGSIM dataset, our model demonstrates superior predictive accuracy over existing methodologies, underscoring the efficacy of Spatiotemporal Transformers in complex dynamic environments.
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Index Terms
- Freeway Trajectory Prediction via SpatiotemporalTransformers
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Published In
March 2024
478 pages
ISBN:9798400716416
DOI:10.1145/3654823
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 29 May 2024
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- Research-article
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China
- Guangzhou Science and Technology Planning Project
- National Natural Science Foundation of China
- Guangdong Basic and Applied Research Foundation
- Nansha Key RD Program
- Science and Technology Planning Project of Guangdong Province
- Guangdong Natural Science Foundation
- Guangdong Basic and Applied Research Foundation
Conference
CACML 2024
CACML 2024: 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
March 22 - 24, 2024
Shanghai, China
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Overall Acceptance Rate 93 of 241 submissions, 39%
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