Driver behavior modeling is an important task for predicting or simulating the evolution of
traffic situations. We investigate the use of Adversarial Inverse Reinforcement Learning …

Optimizing traffic dynamics in an evolving transportation landscape is crucial, particularly in
scenarios where autonomous vehicles (AVs) with varying levels of autonomy coexist with …

For the implementation of autonomous or highly automated driving functions, predicting the
driver behavior of the surrounding road users is highly relevant. This work investigates the …

Modeling the driving behavior of traffic partici-pants in highly interactive traffic situations,
such as roundabouts, poses a significant challenge due to the complex interactions and the …

The aim of the paper is to assess how the traffic of roundabouts could be organized in the
future. A mixed traffic is supposed to occur, featuring both fully automated vehicle and …

The behavior of connected and autonomous vehicles (CAVs) in traffic environments is very
complex. Giving efficient cooperative driving strategies in traffic intersection scenarios is still …

Accurately predicting human driving behavior, particularly in highly interactive traffic
scenarios, poses a significant challenge. In this work, we investigate the application of graph …

Predicting other drivers' trajectories is challenging. We address the issue by introducing a
method to derive a driving policy based on multi-agent reinforcement learning. For this, we …

In recent years, significant advancements have been made in the field of autonomous
driving with the aim of increasing safety and efficiency. However, research that focuses on …

Control design for safe and time-efficient motion of automated vehicles in roundabout
scenarios poses various challenges, especially adaptation to the actual traffic scenario and …