Trajectory prediction of heterogeneous road agents such as vehicles, cyclists, and
pedestrians in dense traffic plays an essential role in self-driving. Despite breakthroughs in …

Accurate trajectory prediction is crucial for ensuring safe and efficient autonomous driving.
However, most existing methods overlook complex interactions between traffic participants …

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 …

Trajectory prediction in urban scenarios is critical for high-level automated driving systems.
However, this task is associated with many challenges. On the one hand, a scene typically …

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 …

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

Accurate prediction of agent motion trajectories is crucial for autonomous driving,
contributing to the reduction of collision risks in human-vehicle interactions and ensuring …

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 …

To drive safely in complex traffic environments, autonomous vehicles need to make an
accurate prediction of the future trajectories of nearby heterogeneous traffic agents (ie …

Predicting future trajectories of surrounding obstacles is a crucial task for autonomous
driving cars to achieve a high degree of road safety. There are several challenges in …