Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction

TL;DR

This paper introduces a spatiotemporal capsule neural network that improves vehicle trajectory prediction by effectively capturing spatial and temporal correlations, enhancing road safety and traffic management in V2X networks.

Contribution

It presents the first hierarchical trajectory prediction model using CapsNet that incorporates geographic, spatial, and temporal vehicle movement data.

Findings

Outperforms existing state-of-the-art methods on real taxi data

Effectively captures local temporal and global spatial correlations

Demonstrates improved prediction accuracy in diverse urban environments

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.