Creating safe autonomous vehicles will require not only extensive training and testing
against realistic operational scenarios, but also dealing with uncertainty. The real world can …

Convolutional neural networks (CNNs) are machine learning models accomplishing state of
the art results in a variety of computer vision tasks, decision making and visual recognition …

Safety is one of the most important development goals for highly automated driving (HAD)
systems. This applies in particular to the perception function driven by Deep Neural …

Applications in the field of Deep Learning are constantly increasing the need for extensive,
annotated data sets. Simulation software offers the possibility to create data sets of almost …

Safety is one of the main challenges that prohibit autonomous vehicles (AV), requiring them
to be well tested ahead of being allowed on the road. In comparison with road tests …

In this work we present an approach for the classification of driving behaviour using
Convolutional Neural Networks (CNNs), based on measurements that have been obtained …

Vision is an important sensing modality in automated driving. Deep learning-based
approaches have gained popularity for different computer vision (CV) tasks such as …

A deep neural network (DNN)-based system is a black box of complex interactions, resulting
in a classification or prediction. We investigate the use of realistic transformations to create …

Recently, autonomous vehicles (AVs) have received a lot of attention from the automotive
industry as well as the AV research community across the globe. To increase the safety of …

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 …