Understanding the surrounding environment is crucial for autonomous vehicles to make
correct driving decisions. In particular, urban scene segmentation is a significant integral …

The existence of real-world adversarial examples (RWAEs)(commonly in the form of
patches) poses a serious threat for the use of deep learning models in safety-critical …

Deep learning and convolutional neural networks allow achieving impressive performance
in computer vision tasks, such as object detection and semantic segmentation (SS) …

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 …

Adversarial attacks have stimulated research interests in the field of deep learning security.
In terms of autonomous driving technology, instance segmentation can help autonomous …

In the development of advanced driver-assistance systems (ADAS) and autonomous
vehicles, machine learning techniques that are based on deep neural networks (DNNs) …

The trend towards autonomous systems in today's technology comes with the need for
environment perception. Deep neural networks (DNNs) constantly showed state-of-the-art …

State-of-the-art deep neural networks have proven to be highly powerful in a broad range of
tasks, including semantic image segmentation. However, these networks are vulnerable …

Recent advances in machine learning, especially techniques such as deep neural networks,
are enabling a range of emerging applications. One such example is autonomous driving …

In autonomous driving (AD), accurate perception is indispensable to achieving safe and
secure driving. Due to its safety-criticality, the security of AD perception has been widely …