Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction
- URL: http://arxiv.org/abs/2303.02880v1
- Date: Mon, 6 Mar 2023 04:15:29 GMT
- Title: Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction
- Authors: Yan Qin, Yong Liang Guan, and Chau Yuen
- Abstract summary: We propose 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.
- Score: 21.6456624219159
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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