Autonomous vehicles currently suffer from a time-inefficient driving style caused by
uncertainty about human behavior in traffic interactions. Accurate and reliable prediction …

Anticipating possible behaviors of traffic participants is an essential capability of
autonomous vehicles. Many behavior detection and maneuver recognition methods only …

The abilities to understand the social interaction behaviors between a vehicle and its
surroundings while predicting its trajectory in an urban environment are critical for road …

Behavior prediction remains one of the most challenging tasks in the autonomous vehicle
(AV) software stack. Forecasting the future trajectories of nearby agents plays a critical role …

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

A major challenge for autonomous vehicles is handling interactions with human-driven
vehicles—for example, in highway merging. A better understanding and computational …

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 …

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

Autonomous vehicles must balance a complex set of objectives. There is no consensus on
how they should do so, nor on a model for specifying a desired driving behavior. We created …

A major challenge for autonomous vehicles is interacting with other traffic participants safely
and smoothly. A promising approach to handle such traffic interactions is equipping …