  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 environment perception being one of its core functions.
Environment perception is usually performed by combining the semantic
information captured by several sensors, i.e., lidar or camera. The semantic
information from the respective sensor can be extracted by using convolutional
neural networks (CNNs) for dense prediction. In the past, CNNs constantly
showed state-of-the-art performance on several vision-related tasks, such as
semantic segmentation of traffic scenes using nothing but the red-green-blue
(RGB) images provided by a camera. Although CNNs obtain state-of-the-art
performance on clean images, almost imperceptible changes to the input,
referred to as adversarial perturbations, may lead to fatal deception. The goal
of this article is to illuminate the vulnerability aspects of CNNs used for
semantic segmentation with respect to adversarial attacks, and share insights
into some of the existing known adversarial defense strategies. We aim to
clarify the advantages and disadvantages associated with applying CNNs for
environment perception in AD to serve as a motivation for future research in
this field.
