The core task of any autonomous driving system is to transform sensory inputs into driving
commands. In end-to-end driving, this is achieved via a neural network, with one or multiple …

Simulation provides an efficient and safe evaluation solution for industrial automation to
pretest software before deploying it in real systems. However, only high-fidelity simulation …

This work introduces interactive traffic scenarios in the CARLA simulator, which are based
on real-world traffic. We concentrate on tactical tasks lasting several seconds, which are …

The idea to generate synthetic data as a tool for broadening access to sensitive microdata
has been proposed for the first time three decades ago. While first applications of the idea …

Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …

Neglecting the effect that decisions have on individuals (and thus, on the underlying data
distribution) when designing algorithmic decision-making policies may increase inequalities …

Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and
significantly aiding in building autonomous systems with a higher level comprehension of …

Deep learning has bolstered gaze estimation techniques, but real-world deployment has
been impeded by inadequate training datasets. This problem is exacerbated by both …

This paper aims to solve the trajectory tracking control problem for an autonomous vehicle
based on reinforcement learning methods. Existing reinforcement learning approaches have …

Transferring the grasping skills learned from simulated environments to the real world is
favorable for many robotic applications, in which the collecting and labeling processes of …