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

In this paper we present the first safe system for full control of self-driving vehicles trained
from human demonstrations and deployed in challenging, real-world, urban environments …

Recognising the goals or intentions of observed vehicles is a key step towards predicting the
long-term future behaviour of other agents in an autonomous driving scenario. When there …

Machine Learning (ML) has replaced traditional handcrafted methods for perception and
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …

Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-
generated trajectories are the most widely used approaches for training modern embodied …

Motion prediction of road users in traffic scenes is critical for autonomous driving systems
that must take safe and robust decisions in complex dynamic environments. We present a …

Learning-based approaches have achieved remarkable performance in the domain of
autonomous driving. Leveraging the impressive ability of neural networks and large …

Predicting the future motion of observed vehicles is a crucial enabler for safe autonomous
driving. The field of motion prediction has seen large progress recently with State-of-the-Art …

This article examines an online distributed optimization problem over an unbalanced
digraph, in which a group of nodes in the network tries to collectively search for a minimizer …

Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-
generated trajectories are the most widely used approaches for training modern embodied …