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 …

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) …

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

Deep neural networks (DNNs) have accomplished impressive success in various
applications, including autonomous driving perception tasks, in recent years. On the other …

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 …

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

With rapid development of self-driving vehicles, recent work in adversarial machine learning
started to study adversarial examples (AEs) for perception of autonomous driving (AD) …

Recent advances in machine learning, especially techniques such as deep neural networks,
are promoting a range of high-stakes applications, including autonomous driving, which …

Although deep neural networks (DNNs) are high-performance methods for various complex
tasks, eg, environment perception in automated vehicles (AVs), they are vulnerable to …