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 …

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

While autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient, transferable, and …

Predicting and planning interactive behaviors in complex traffic situations presents a
challenging task. Especially in scenarios involving multiple traffic participants that interact …

Behavior prediction of traffic actors is an essential component of any real-world self-driving
system. Actors' long-term behaviors tend to be governed by their interactions with other …

Interactive driving scenarios, such as lane changes, merges and unprotected turns, are
some of the most challenging situations for autonomous driving. Planning in interactive …

When autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient transferable and …

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to
overcome on the journey to fully autonomous vehicles. To address this challenge, we …

To plan safe maneuvers and act with foresight, autonomous vehicles must be capable of
accurately predicting the uncertain future. In the context of autonomous driving, deep neural …

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a
formidable challenge, especially in mixed autonomy environments. Traditional approaches …