We propose a motion forecasting model that exploits a novel structured map representation
as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we …

Human motion prediction aims to forecast future human poses given a past motion. Whether
based on recurrent or feed-forward neural networks, existing methods fail to model the …

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 …

In order to plan a safe maneuver an autonomous vehicle must accurately perceive its
environment, and understand the interactions among traffic participants. In this paper, we …

Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute
actions. Our work addresses two key challenges in trajectory prediction, learning multimodal …

This study explores the application of self-supervised learning (SSL) to the task of motion
forecasting, an area that has not yet been extensively investigated despite the widespread …

Motion prediction is essential and challenging for autonomous vehicles and social robots.
One challenge of motion prediction is to model the interaction among traffic actors, which …

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

Trajectory prediction plays a crucial role in bridging the gap between perception and
planning in autonomous driving systems. However, most existing methods perform motion …

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