Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

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 …

While automated driving is often advertised with better-than-human driving performance, this
chapter reviews that it is nearly impossible to provide direct statistical evidence on the …

While neural networks trained for semantic segmentation are essential for perception in
autonomous driving, most current algorithms assume a fixed number of classes, presenting …

While current approaches for neural network training often aim at improving performance,
less focus is put on training methods aiming at robustness towards varying noise conditions …

Enabling autonomous driving (AD) can be considered one of the biggest challenges in
today? s technology. AD is a complex task accomplished by several functionalities, with …

Deep neural networks have achieved promising results in a breadth of medical image
segmentation tasks. Nevertheless, they require large training datasets with pixel-wise …

Adversarial training is promising for improving robustness of deep neural networks towards
adversarial perturbations, especially on the classification task. The effect of this type of …

Commonly used metrics to evaluate semantic segmentation such as mean intersection over
union (mIoU) do not incorporate temporal consistency. A straightforward extension of …

The robustness of deep neural networks is an increasingly essential issue as they become
more and more prevalent in several real-world applications like autonomous vehicles. If …