Autonomous vehicles are expected to drive in complex scenarios with several independent
non cooperating agents. Path planning for safely navigating in such environments can not …

Pedestrians and drivers are expected to safely navigate complex urban environments along
with several non cooperating agents. Autonomous vehicles will soon replicate this …

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

Predicting the trajectories of road agents is essential for autonomous driving systems. The
recent mainstream methods follow a static paradigm which predicts the future trajectory by …

Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …

Anticipating human motion in crowded scenarios is essential for developing intelligent
transportation systems, social-aware robots and advanced video surveillance applications …

Existing trajectory prediction methods for autonomous driving typically rely on one-stage
trajectory prediction models which condition future trajectories on observed trajectories …

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 …

We propose advances that address two key challenges in future trajectory prediction:(i)
multimodality in both training data and predictions and (ii) constant time inference …

Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …