Automated creation of synthetic traffic scenarios is a key part of scaling the safety validation
of autonomous vehicles (AVs). In this paper, we propose Scenario Diffusion, a novel …

Predicting future trajectories of surrounding agents is essential for safety-critical autonomous
driving. Most existing work focuses on predicting marginal trajectories for each agent …

Background: In the context of Warehouse Management Systems, knowledge related to
motion trajectory prediction methods utilizing machine learning techniques seems to be …

Predicting the future motion of road agents is a critical task in an autonomous driving
pipeline. In this work, we address the problem of generating a set of scene-level, or joint …

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 …

The historical trajectories previously passing through a location may help infer the future
trajectory of an agent currently at this location. Despite great improvements in trajectory …

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 …

High-definition (HD) maps have played an integral role in the development of modern
autonomous vehicle (AV) stacks albeit with high associated labeling and maintenance costs …

Predicting the future behavior of agents is a fundamental task in autonomous vehicle
domains. Accurate prediction relies on comprehending the surrounding map, which …

In recent years, there has been a rapid rise in interest in trajectory prediction in the field of
autonomous driving. However, the domain generalization of current works is often …