State of the art emergency brake assistant systems solely based on sensor measurements
reduced the number of traffic accidents and casualties drastically in recent years. In order to …

Recent advances in the field of environment perception and cognition enable automated
vehicles to safely drive in a growing variety of complex situations. However, in situations …

In recent years, automated driving has gained the public spotlight as more prototypes are
tested amongst human drivers. Since driving is a social behavior, automated vehicles (AVs) …

In this work, we present a decision-making system for automated vehicles driving in highway
environments. The task is modeled as a Partially Observable Markov Decision Process, in …

Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with
other traffic participants with limited perception ability. Current deployed approaches relying …

We present a novel approach for vehicle-pedestrian collision risk analysis that incorporates
mutual situational awareness, a degree of potential motion coupling and the spatial layout of …

Autonomous vehicles face big challenges guaranteeing provable safety during driving. One
of the major problems is the uncertainty arising from the perception of the surrounding …

In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …

Automation of complex traffic scenarios is expected to rely on input from a roadside
infrastructure to complement the vehicles' environment perception. We here explore design …

Autonomous vehicles need to perform social accepted behaviors in complex urban
scenarios including human‐driven vehicles with uncertain intentions. This leads to many …