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

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

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 …

Automated driving has the potential to revolutionize personal, public, and freight mobility.
Beside accurately perceiving the environment, automated vehicles must plan a safe …

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

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 …

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 …

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

Trajectory sampling in the Frenet (road-aligned) frame, is one of the most popular methods
for motion planning of autonomous vehicles. It operates by sampling a set of behavioral …

Autonomous vehicle research has grown exponentially over the years with researchers
working on different object detection algorithms to realize safe and competent self-driving …