Tactical decision making for autonomous driving is challenging due to the diversity of
environments, the uncertainty in the sensor information, and the complex interaction with …

Automated driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …

The validity of any traffic simulation model depends on its ability to generate representative
driver acceleration profiles. This paper studies the effectiveness of recurrent neural networks …

Reinforcement learning (RL) can be used to create a tactical decision-making agent for
autonomous driving. However, previous approaches only output decisions and do not …

Human drivers use nonverbal communication and anticipation of other drivers' actions to
master conflicts occurring in everyday driving situations. Without a high penetration of …

There have been numerous advances in reinforcement learning, but the typically
unconstrained exploration of the learning process prevents the adoption of these methods in …

Safety is the cornerstone of autonomous driving vehicles. For autonomously controlled
vehicles driving safely in complex and dynamic traffic scenarios, it is essential to precisely …

Autonomous driving requires efficient and safe decision making and motion planning in
dynamic and uncertain environments. Future movement of surrounding vehicles is often …

For a decade, researchers have focused on the development and deployment of road
automated mobility. In the development of autonomous driving embedded systems, several …

To enable highly automated vehicles where the driver is no longer a safety backup, the
vehicle must deal with various Functional Insufficiencies (FIs). Thus-far, there is no widely …