With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that
focuses on the development of autonomous vehicles (AVs) that are capable of interacting …

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 …

Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an
important aspect of safety and planning efficiency for autonomous vehicles. With recent …

Trajectory prediction is a crucial step in the pipeline for autonomous driving because it not
only improves the planning of future routes, but also ensures vehicle safety. On the basis of …

In conventional safety analysis, traffic and crash data are often aggregated at the
geographical units like census tracts, street blocks, and traffic analysis zones, which are …

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of
intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets …

Trajectory prediction of the ego vehicle is necessary for the cooperation driving of intelligent
vehicles and drivers. Methods based on deep learning can fit complex functions, but they …

Accurate prediction of vehicle trajectories is crucial to the safety and comfort of autonomous
vehicles. Although several graph-based models have exhibited substantial progress in …