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.

翻译：通过车联网（V2X）技术的进步，道路安全、能源消耗和交通效率可得到显著提升。准确的车辆轨迹预测有助于V2X网络实时应用中的通信流量管理和网络资源分配。近期研究报道了利用循环神经网络及其变体预测车辆移动性。然而，车辆移动行为的空间属性被忽视，导致信息利用不完整。为弥补这一不足，我们首次提出一种基于胶囊神经网络（CapsNet）的分层轨迹预测结构，该结构包含三个顺序组件。首先，将地理信息转换为网格地图表示，从时空维度描述车辆移动性分布。其次，CapsNet作为核心模型，通过分层胶囊嵌入局部时间与全局空间相关性。最后，基于在葡萄牙波尔图市和新加坡采集的真实出租车移动数据进行的广泛实验表明，所提方法优于现有最先进方法。