The evolution of automotive technology will eventually permit the automated driving system
on the vehicle to handle all circumstances. Human occupants will be just passengers. This …

There is considerable evidence that deep neural networks are vulnerable to adversarial
perturbations applied directly to their digital inputs. However, it remains an open question …

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 …

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 …

Autonomous vehicles increasingly utilize the vision-based perception module to acquire
information about driving environments and detect obstacles. Correct detection and …

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

ABSTRACT Connected and Autonomous Vehicles (CAVs) offer improved efficiency and
convenience through innovative embedded devices. However, the development of these …

Physical adversarial attacks directly apply adversarial perturbations to real-world objects.
Perturbations usually are printed as patches and pasted on target objects. This requires …

It has been shown that most machine learning algorithms are susceptible to adversarial
perturbations. Slightly perturbing an image in a carefully chosen direction in the image …

Robust perception is crucial for autonomous vehicle security. In this work, we design a
practical adversarial patch attack against camera-based obstacle detection. We identify that …