Deep neural networks (DNNs) are being incorporated into various autonomous systems like
self-driving cars and robots. However, there is a rising concern about the robustness of …

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

As research in deep neural networks advances, deep convolutional networks become
promising for autonomous driving tasks. In particular, there is an emerging trend of …

The success of deep neural networks (DNNs) has led to its increased deployment in various
real-world applications, which provides strong incentives for motivated adversaries to …

In an end-to-end vehicle control scenario, where a deep neural network is trained on visual
input solely, adversarial vulnerability leaves a possibility to manipulate the steering …

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

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 network is the main research branch in artificial intelligence and suitable for
many decision-making fields. Autonomous driving and unmanned vehicle often depend on …

In recent years, many deep learning models have been adopted in autonomous driving. At
the same time, these models introduce new vulnerabilities that may compromise the safety …

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