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.

翻译：减少交通事故的主要途径之一是通过驾驶员辅助系统或完全自主系统提高车辆安全性。在这类系统中，障碍物检测与分割任务（尤其是基于深度学习的方法）对于正确、安全的导航场景理解起着基础性作用。此外，现代车辆中传感器的多样性为在照明和天气不利条件下导航等挑战性场景中提升感知鲁棒性提供了丰富的替代方案。尽管当前对该主题的关注度较高，但文献中缺乏对基于雷达以及雷达-相机融合感知的研究。因此，本文旨在对ADAS及自动驾驶中基于相机和雷达的感知现状进行系统研究。文中介绍了两种传感器及其融合的相关概念与特性，同时概述了基于深度学习的检测与分割任务，以及车辆感知领域的主要数据集、评价指标、挑战与待解决问题。