Multimodal prediction results are essential for trajectory prediction task as there is no single
correct answer for the future. Previous frameworks can be divided into three categories …

Capturing high-dimensional social interactions and feasible futures is essential for
predicting trajectories. To address this complex nature, several attempts have been devoted …

Variational autoencoder (VAE) has widely been utilized for modeling data distributions
because it is theoretically elegant, easy to train, and has nice manifold representations …

Pedestrian trajectory prediction is critical in many vision tasks but challenging due to the
multimodality of the future trajectory. Most existing methods predict multimodal trajectories …

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 …

Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately
anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable …

Trajectory prediction is an important task to support safe and intelligent behaviours in
autonomous systems. Many advanced approaches have been proposed over the years with …

Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of
future dynamics with both diversity and accuracy. In this paper, we propose a distribution …

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 …

Pedestrian trajectory prediction in crowd scenes plays a significant role in intelligent
transportation systems. The main challenges are manifested in learning motion patterns and …