When driving in urban environments, an autonomous vehicle must account for the
interaction with other traffic participants. It must reason about their future behavior, how its …

Planning for autonomous driving in complex, urban scenarios requires accurate prediction
of the trajectories of surrounding traffic participants. Their future behavior depends on their …

Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

Motion planning algorithms for urban automated driving must handle uncertainty due to
unknown intention and future motion of Dynamic Obstacles (DOs). Considering a single …

Addressing safe and efficient interaction between connected and automated vehicles
(CAVs) and human-driven vehicles in a mixed-traffic environment has attracted considerable …

Given the current capabilities of autonomous vehicles, one can easily imagine autonomous
vehicles being released on the road in the near future. However, it can be assumed that this …

It is essential for autonomous driving cars to understand and predict other surrounding cars'
behaviors, especially in urban environments, due to the high traffic volumes and complex …

In behavior planning for autonomous vehicles, other traffic participants have to be
considered. This is typically achieved by using a prediction model to estimate their future …

Human drivers have complex and individual behavior characteristics which describe how
they act in a specific situation. Accurate behavior models are essential for many applications …

To operate reliably in real-world traffic, an autonomous car must evaluate the consequences
of its potential actions by anticipating the uncertain intentions of other traffic participants. This …