Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …

Predicting the future motion of dynamic agents is of paramount importance to ensuring
safety and assessing risks in motion planning for autonomous robots. In this study, we …

The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety
of autonomous driving. Recent motion prediction approaches attempt to achieve such …

Predicting human motion from a historical pose sequence is at the core of many applications
in computer vision. Current state-of-the-art methods concentrate on learning motion contexts …

Human trajectory forecasting is a key component of autonomous vehicles, social-aware
robots and advanced video-surveillance applications. This challenging task typically …

To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of
other agents around it. Motion prediction is an extremely challenging task that recently …

Motion prediction is crucial for autonomous driving systems to understand complex driving
scenarios and make informed decisions. However, this task is challenging due to the diverse …

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to
make safe decisions. Existing works explore to directly predict future trajectories based on …

Trajectory prediction is critical for applications of planning safe future movements and
remains challenging even for the next few seconds in urban mixed traffic. How an agent …