Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …

Autonomous Vehicles have wide-ranging applications in off-road environments. Off-road
vehicular scenes can be abstracted as multi-agent systems, and trajectory prediction is a …

Autonomous driving promises to be the main trend in the future intelligent transportation
systems due to its potentiality for energy saving, and traffic and safety improvements …

Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …

Trajectory prediction of heterogeneous road agents such as vehicles, cyclists, and
pedestrians in dense traffic plays an essential role in self-driving. Despite breakthroughs in …

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 …

In this work, we propose a novel approach for integrating rules into traffic agent trajectory
prediction. Consideration of rules is important for understanding how people behave-yet, it …

Predicting the trajectories of surrounding heterogeneous traffic agents is critical for the
decision making of an autonomous vehicle. Recently, many existing prediction methods …

Autonomous vehicles must balance a complex set of objectives. There is no consensus on
how they should do so, nor on a model for specifying a desired driving behavior. We created …

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …