Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/166621
Title: Spatiotemporal capsule neural network for vehicle trajectory prediction
Authors: Qin, Yan
Guan, Yong Liang
Yuen, Chau
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2023
Source: 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
Project: A19D6a0053 
Journal: IEEE Transactions on Vehicular Technology 
Abstract: 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.
URI: https://hdl.handle.net/10356/166621
ISSN: 0018-9545
DOI: 10.1109/TVT.2023.3253695
Schools: School of Electrical and Electronic Engineering 
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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