Motion prediction is the fundamental input for decision-making in autonomous vehicles. The
current motion prediction solutions are designed with a strong reliance on black box …

Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …

Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

Automated vehicles rely heavily on data-driven methods, especially for complex urban
environments. Large datasets of real world measurement data in the form of road user …

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 …

Encoding a driving scene into vector representations has been an essential task for
autonomous driving that can benefit downstream tasks eg, trajectory prediction. The driving …

In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework
allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories. We …

The development of safety-oriented research and applications requires fine-grain vehicle
trajectories that not only have high accuracy, but also capture substantial safety-critical …

The development and validation of automated vehicles involves a large number of
challenges to be overcome. Due to the high complexity, many classic approaches quickly …