This paper provides a comprehensive survey of research on self-driving cars, focusing on those developed since the DARPA challenges, which are equipped with an autonomy system categorized as SAE level 3 or higher. The architecture of the autonomy system is typically divided into two main parts: the perception system and the decision-making system. The perception system includes subsystems for tasks such as localization, static obstacle mapping, road mapping, moving obstacle detection and tracking, and traffic signalization detection and recognition. The decision-making system includes subsystems for tasks such as route planning, path planning, behavior selection, motion planning, obstacle avoidance, and control. The paper reviews relevant methods for perception and decision-making, and provides a detailed description of the architecture of the autonomy system of the Intelligent Autonomous Robotics Automobile (IARA) developed at the Universidade Federal do Espírito Santo (UFES). It also lists prominent self-driving car research platforms developed by academia and technology companies, including notable examples from universities and companies such as Stanford University, Carnegie Mellon University, Google, Uber, Baidu, Tesla, and Nvidia. The survey covers various localization methods, including LIDAR-based, LIDAR plus camera-based, and camera-based approaches. It discusses offline and online mapping techniques, such as occupancy grid maps, remission maps, and landmark maps. The paper also explores road mapping techniques, including metric and topological representations, and methods for creating road maps from aerial images. Overall, the paper provides a detailed overview of the current state of self-driving car technology, highlighting the key components and methods used in their development.This paper provides a comprehensive survey of research on self-driving cars, focusing on those developed since the DARPA challenges, which are equipped with an autonomy system categorized as SAE level 3 or higher. The architecture of the autonomy system is typically divided into two main parts: the perception system and the decision-making system. The perception system includes subsystems for tasks such as localization, static obstacle mapping, road mapping, moving obstacle detection and tracking, and traffic signalization detection and recognition. The decision-making system includes subsystems for tasks such as route planning, path planning, behavior selection, motion planning, obstacle avoidance, and control. The paper reviews relevant methods for perception and decision-making, and provides a detailed description of the architecture of the autonomy system of the Intelligent Autonomous Robotics Automobile (IARA) developed at the Universidade Federal do Espírito Santo (UFES). It also lists prominent self-driving car research platforms developed by academia and technology companies, including notable examples from universities and companies such as Stanford University, Carnegie Mellon University, Google, Uber, Baidu, Tesla, and Nvidia. The survey covers various localization methods, including LIDAR-based, LIDAR plus camera-based, and camera-based approaches. It discusses offline and online mapping techniques, such as occupancy grid maps, remission maps, and landmark maps. The paper also explores road mapping techniques, including metric and topological representations, and methods for creating road maps from aerial images. Overall, the paper provides a detailed overview of the current state of self-driving car technology, highlighting the key components and methods used in their development.