In this review, we provide an overview of emerging trends and challenges in the field of
intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, …

Information gathering while interacting with other agents under sensing and motion uncertainty
is critical in domains such as driving, service robots, racing, or surveillance. The interests …

Deployment of autonomous vehicles on public roads promises increased efficiency and safety.
It requires understanding the intent of human drivers and adapting to their driving styles. …

Current state-of-the-art vehicle safety systems, such as assistive braking or automatic lane
following, are still only able to help in relatively simple driving situations. We introduce a …

Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains,
where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, …

Recent research has highlighted the vulnerabilities of modern machine learning based
systems to bias, especially towards segments of society that are under-represented in training …

We present a three-dimensional deep convolutional neural network (3D CNN) approach for
grasping unknown objects with soft hands. Soft hands are compliant and capable of …

High-end vehicles are already equipped with safety systems, such as assistive braking and
automatic lane following, enhancing vehicle safety. Yet, these current solutions can only help …

Simulation has the potential to transform the development of robust algorithms for mobile
agents deployed in safety-critical scenarios. However, the poor photorealism and lack of …

In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are
separated, limiting the planner to react to predictions that are not informed by the planned …