The ability to predict the future trajectories of traffic participants is crucial for the safe and
efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model …

Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating
through complex urban traffic scenes. Bird's-eye-view roadmap information provides …

Predicting the motion of surrounding vehicles is an important function of autonomous
vehicles. However, most of the current state-of-the-art trajectory prediction models rely …

Over the past few years there is a growing interest in the learning-based self driving system.
To ensure safety, such systems are first developed and validated in simulators before being …

Accurate prediction of the future trajectories of traffic agents is a critical aspect of
autonomous vehicle navigation. However, most existing approaches focus on predicting …

Automated vehicles and advanced driver-assistance systems require an accurate prediction
of future traffic scene states. The tendency in recent years has been to use deep learning …

To handle two shortcomings of existing methods,(i) nearly all models rely on the high-
definition (HD) maps, yet the map information is not always available in real traffic scenes …

Predicting the future trajectories of dynamic traffic actors is the Gordian knot for autonomous
vehicles to achieve collision-free driving. Most existing works suffer from a gap in …

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

Predicting the trajectories of road agents is essential for autonomous driving systems. The
recent mainstream methods follow a static paradigm which predicts the future trajectory by …