Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

Artificial intelligence can be used to realise new types of protective devices and assistance
systems, so their importance for occupational safety and health is continuously increasing …

Abstract Deep Neural Networks (DNNs) demonstrated remarkable capabilities in learning
complex hierarchical data representations, but the nature of these representations remains …

Deployment of modern data-driven machine learning methods, most often realized by deep
neural networks (DNNs), in safety-critical applications such as health care, industrial plant …

Safety assurance of automated driving systems must consider uncertain environment
perception. This paper reviews literature addressing how perception testing is realized as …

Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …

Availability of powerful computation and communication technology as well as advances in
artificial intelligence enable a new generation of complex, AI-intense systems and …

In safety-critical applications such as automated driving, perception errors may create an
imminent risk to vulnerable road users (VRU). To mitigate the occurrence of unexpected and …

Detecting out-of-distribution (OOD) samples is vital for developing machine learning based
models for critical safety systems. Common approaches for OOD detection assume access …

Recent advances in the field of deep learning and impressive performance of deep neural
networks (DNNs) for perception have resulted in an increased demand for their use in …