Spatiotemporal capsule neural network for vehicle trajectory prediction
Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of...
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sg-ntu-dr.10356-1666212023-05-05T15:47:02Z Spatiotemporal capsule neural network for vehicle trajectory prediction Qin, Yan Guan, Yong Liang Yuen, Chau School of Electrical and Electronic Engineering Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Trajectory Vehicle-to-Everything Global Positioning System Task Analysis Predictive Models Real-Time Systems Hidden Markov Models Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for the first time a hierarchical trajectory prediction structure using the capsule neural network (CapsNet) with three sequential components. First, the geographic information is transformed into a grid map presentation, describing vehicle mobility distribution spatially and temporally. Second, CapsNet serves as the core model to embed local temporal and global spatial correlation through hierarchical capsules. Finally, extensive experiments conducted on actual taxi mobility data collected in Porto city (Portugal) and Singapore show that the proposed method outperforms the state-of-the-art methods. Agency for Science, Technology and Research (A*STAR) Submitted/Accepted version This research is supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund-Pre Positioning (IAF-PP) (Grant No. A19D6a0053). 2023-05-04T06:47:03Z 2023-05-04T06:47:03Z 2023 Journal Article Qin, Y., Guan, Y. L. & Yuen, C. (2023). Spatiotemporal capsule neural network for vehicle trajectory prediction. IEEE Transactions On Vehicular Technology. https://dx.doi.org/10.1109/TVT.2023.3253695 0018-9545 https://hdl.handle.net/10356/166621 10.1109/TVT.2023.3253695 2-s2.0-85149874574 en A19D6a0053 IEEE Transactions on Vehicular Technology © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TVT.2023.3253695. application/pdf |
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Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Trajectory Vehicle-to-Everything Global Positioning System Task Analysis Predictive Models Real-Time Systems Hidden Markov Models |
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Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Trajectory Vehicle-to-Everything Global Positioning System Task Analysis Predictive Models Real-Time Systems Hidden Markov Models Qin, Yan Guan, Yong Liang Yuen, Chau Spatiotemporal capsule neural network for vehicle trajectory prediction |
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Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for the first time a hierarchical trajectory prediction structure using the capsule neural network (CapsNet) with three sequential components. First, the geographic information is transformed into a grid map presentation, describing vehicle mobility distribution spatially and temporally. Second, CapsNet serves as the core model to embed local temporal and global spatial correlation through hierarchical capsules. Finally, extensive experiments conducted on actual taxi mobility data collected in Porto city (Portugal) and Singapore show that the proposed method outperforms the state-of-the-art methods. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Qin, Yan Guan, Yong Liang Yuen, Chau |
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Article |
author |
Qin, Yan Guan, Yong Liang Yuen, Chau |
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Qin, Yan |
title |
Spatiotemporal capsule neural network for vehicle trajectory prediction |
title_short |
Spatiotemporal capsule neural network for vehicle trajectory prediction |
title_full |
Spatiotemporal capsule neural network for vehicle trajectory prediction |
title_fullStr |
Spatiotemporal capsule neural network for vehicle trajectory prediction |
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Spatiotemporal capsule neural network for vehicle trajectory prediction |
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spatiotemporal capsule neural network for vehicle trajectory prediction |
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2023 |
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https://hdl.handle.net/10356/166621 |
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1770564905360949248 |