Accurately predicting the trajectory of road agents in complex traffic scenarios is challenging
because the movement patterns of agents are complex and stochastic, not only depending …

It is critical for autonomous vehicles to accurately forecast the future trajectories of
surrounding agents to avoid collisions. However, capturing the complex interactions …

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

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 …

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

Multi-agent trajectory prediction is a challenging task because of the uncertainty of agents'
behaviors, interactions between agents, complex road geometry in urban environments, and …

For autonomous vehicles driving on roads, future trajectories of surrounding traffic agents
(eg, vehicles, bicycles, pedestrians) are essential information. The prediction of future …

In dynamic and interactive autonomous driving scenarios, accurately predicting the future
movements of vehicle agents is crucial. However, current methods often fail to capture …

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 the safe and efficient operation of connected automated vehicles under complex driving …