Automated driving is a promising tool for reducing traffic accidents. While some companies
claim that many cutting-edge automated driving functions have been developed, how to …

Autonomous driving related researches require the analysis and usage of massive amounts
of driving scenario data. Compared to raw data collected by sensors, scenario data provide …

Autonomous vehicles (AVs) are emerging in the automobile industry with potential benefits
to reduce traffic congestion, improve mobility and accessibility, as well as safety. According …

For the classification of traffic scenes, a description model is necessary that can describe the
scene in a uniform way, independent of its domain. A model to describe a traffic scene in a …

Verifying the safety of autonomous vehicles is one of the major challenges towards their
deployment on public roads due to the vast number of possible situations that can occur in …

Automated identification of the relationships between traffic actors and surrounding objects,
in order to describe their behavior and predict their intentions, has become the focus of …

This paper presents a white-box intention-aware decision-making for the handling of
interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street …

Reproducing real-world traffic scenes in the simulator is fundamental to training self-driving
systems. Creating a simulation scenario is a complex task, generally done manually: the ego …

One core challenge in the development of automated vehicles is their capability to deal with
a multitude of complex traffic scenarios with many, hard to predict traffic participants. As part …

Proving grounds and test areas equipped with smart infrastructure might provide a solution
to bridge the gap between laboratory testbeds and real-life testing by challenging the …