In recent years, various domains have been influenced by the rapid growth of machine
learning. Autonomous driving is an area that has tremendously developed in parallel with …

Autonomous driving (AD) has developed tremendously in parallel with the ongoing
development and improvement of deep learning (DL) technology. However, the uptake of …

Boosted by the evolution of machine learning technology, large amount of data and
advanced computing system, neural networks have achieved state-of-the-art performance …

Abstract Autonomous Vehicles (CAVs) are currently seen as a viable alternative to
traditional vehicles. However, CAVs will face serious cyber threats because many …

Nowadays, autonomous driving has attracted much attention from both industry and
academia. Convolutional neural network (CNN) is a key component in autonomous driving …

Recently, many applications have begun to employ deep neural networks (DNN), such as
image recognition and safety-critical applications, for more accurate results. One of the most …

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) …

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

Autonomous vehicles rely on computer vision models for perception, which have been
shown to be vulnerable to adversarial attacks. These attacks pose various risks from …

The perception system is a safety-critical component that directly impacts the overall safety
of autonomous driving systems (ADSs). It is imperative to ensure the robustness of the deep …