Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …

Forecasting future trajectories of agents in complex traffic scenes requires reliable and
efficient predictions for all agents in the scene. However, existing methods for trajectory …

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

Predicting diverse yet admissible trajectories that adhere to the map constraints is
challenging. Graph-based scene encoders have been proven effective for preserving local …

The task of motion forecasting is critical for self-driving vehicles (SDV s) to be able to plan a
safe maneuver. Towards this goal, modern approaches reason about the map, the agents' …

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 …

Trajectory prediction is a crucial undertaking in understanding entity movement or human
behavior from observed sequences. However, current methods often assume that the …

Lane graph estimation is an essential and highly challenging task in automated driving and
HD map learning. Existing methods using either onboard or aerial imagery struggle with …

Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an
important aspect of safety and planning efficiency for autonomous vehicles. With recent …

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 …