Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …

Trajectory prediction has been a crucial task in building a reliable autonomous driving
system by anticipating possible dangers. One key issue is to generate consistent trajectory …

Abstract Best-of-N (BoN) Average Displacement Error (ADE)/Final Displacement Error (FDE)
is the most used metric for evaluating trajectory prediction models. Yet, the BoN does not …

Predicting future motions of road participants is an important task for driving autonomously in
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …

Accurate long-term trajectory prediction in complex scenes, where multiple agents (eg,
pedestrians or vehicles) interact with each other and the environment while attempting to …

Safety-critical applications such as autonomous vehicles and social robots require fast
computation and accurate probability density estimation on trajectory prediction. To address …

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 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 …

Predicting the future trajectories of the traffic agents is a gordian technique in autonomous
driving. However, trajectory prediction suffers from data imbalance in the prevalent datasets …

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