Automated driving systems need to perform according to what human drivers expect in every
situation. A different behavior can be wrongly interpreted by other human drivers and cause …

Predicting the future trajectories of surrounding vehicles is an important challenge in
automated driving, especially in highly interactive environments such as roundabouts. Many …

Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous
driving systems in simulation. This work introduces a data-driven method called TrafficGen …

When autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient transferable and …

Traffic simulation plays a crucial role in evaluating and improving autonomous driving
planning systems. After being deployed on public roads, autonomous vehicles need to …

To operate reliably in real-world traffic, an autonomous car must evaluate the consequences
of its potential actions by anticipating the uncertain intentions of other traffic participants. This …

There is currently a huge interest around autonomous vehicles from both industry and
academia. This is mainly due to recent advances in machine learning and deep learning …

In behavior planning for autonomous vehicles, other traffic participants have to be
considered. This is typically achieved by using a prediction model to estimate their future …

In this paper, we propose a decision making algorithm intended for automated vehicles that
negotiate with other possibly non-automated vehicles in intersections. The decision …

While autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient, transferable, and …