… internet of vehicles (IoV). With the help of deep learning and big data, it is possible to understand
the between-vehicle … spatialtemporal dynamic attention network for vehicle trajectory pre…

… [33], text summarization [34], and trajectory prediction [35]. For efficiently solving the high-…
dynamically paying attention to surrounding vehicles’ motion, we also apply the attention …

… Inspired by people’s natural habit of navigating traffic with attention to their … dynamic graph
attention network to solve all those challenges. The network is designed to model the dynamic …

… attention mechanism to model dynamic spatial dependencies of trajectory data in road
networks, and a temporal attention … Compared with RNNs and CNNs, selfattention networks are …

… This paper investigates vehicle trajectory prediction problems in real traffic scenarios by …
between multiple vehicles. The existing GCN-based trajectory predictions are often considered …

… We follow [23] and, in order to take into account the social effect of the surrounding vehicles
on the prediction target based on relative dynamics, we include additional information (…

… a novel trajectory prediction model and a modeling method of vehicle interaction in dynamic
… In this paper, a new neural network model for trajectory prediction is proposed. In terms of …

… [41] proposed a novel heterogeneous edge-enhanced graph network to predict vehicle
trajectories, which extracted dynamic vehicles information from historical states to generate the …

… [21] propose a deep stochastic inverse optimal control RNN encoder–decoder framework
to predict the trajectories of interacting road users in dynamic scenes. Kim et al. [22] propose …

… -temporal dynamic attention network for vehicle trajectory prediction … LSTM and attention
mechanism are utilized … prediction and single-modal prediction models proposed in this study. …