This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully
harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN …

The accurate trajectory prediction of surrounding vehicles is crucial for the sustainability and
safety of connected and autonomous vehicles under mixed traffic streams in the real world …

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …

In vehicle trajectory prediction, the difficulty in modeling the interaction relationship between
vehicles lies in constructing the interaction structure between the vehicles in the traffic …

Vehicle trajectory prediction is a challenging problem in the field of autonomous driving,
which is of great significance to the safety of autonomous driving and traffic roads. In view of …

Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …

Predicting the motion trajectories of moving agents in complex traffic scenes, such as
crossroads and roundabouts, plays an important role in cooperative intelligent transportation …

Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …

Accurate and efficient traffic prediction is the key to the realization of intelligent transportation
system (ITS), which helps to alleviate traffic congestion and reduce traffic accidents. Due to …

In the context of the booming Internet of Vehicles, predicting vehicle trajectories is crucial for
intelligent transportation systems. Existing methods, reliant on sensor data and behavior …