The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …

In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …

Human errors contribute to 94%(±2.2%) of road crashes resulting in fatal/non-fatal
causalities, vehicle damages and a predicament in the pathway to safer road systems …

Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic
safety and efficiency, contributing significantly to modern transportation. The integration of …

With the increasing deployment of roadside sensors, vehicle trajectories can be collected for
driving behavior analysis and vehicle-highway automation systems. However, due to …

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 …

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 …

Integrating trajectory prediction to the decision-making and planning modules of modular
autonomous driving systems is expected to improve the safety and efficiency of self-driving …