  One of the main paths towards the reduction of traffic accidents is the
increase in vehicle safety through driver assistance systems or even systems
with a complete level of autonomy. In these types of systems, tasks such as
obstacle detection and segmentation, especially the Deep Learning-based ones,
play a fundamental role in scene understanding for correct and safe navigation.
Besides that, the wide variety of sensors in vehicles nowadays provides a rich
set of alternatives for improvement in the robustness of perception in
challenging situations, such as navigation under lighting and weather adverse
conditions. Despite the current focus given to the subject, the literature
lacks studies on radar-based and radar-camera fusion-based perception. Hence,
this work aims to carry out a study on the current scenario of camera and
radar-based perception for ADAS and autonomous vehicles. Concepts and
characteristics related to both sensors, as well as to their fusion, are
presented. Additionally, we give an overview of the Deep Learning-based
detection and segmentation tasks, and the main datasets, metrics, challenges,
and open questions in vehicle perception.
