Connected and autonomous vehicles (CAVs) will form the backbone of future next-
generation intelligent transportation systems (ITS) providing travel comfort, road safety …

Connected and autonomous vehicles (CAV aka driverless cars) offset human response for
transportation infrastructure, enhancing traffic efficiency, travel leisure, and road safety …

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

Deep learning has an impressive performance of object detection and classification for
autonomous vehicles. Nevertheless, the essential vulnerability of deep learning models to …

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

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

Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-
driving cars. However, the security of DNN models in this context leads to major safety …

The emergence of in-vehicle networks, which are composed of controller area network
(CAN) buses and a great number of ECUs, significantly reduces the difficulty of vehicle …

The rapid development of artificial intelligence, especially deep learning technology, has
advanced autonomous driving systems (ADSs) by providing precise control decisions to …

With the increasing sophistication of cyber threats, the integration of machine learning (ML)
techniques in network security has become imperative for detecting and mitigating evolving …