Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction
This work addresses trajectory prediction for V2X networks to improve road safety and traffic efficiency, representing an incremental advance by integrating spatial information into existing methods.
The paper tackled vehicle trajectory prediction by proposing a hierarchical structure using a capsule neural network to incorporate spatial attributes, achieving state-of-the-art performance on real taxi mobility datasets from Porto and Singapore.
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.