# Self-Driving Cars: A Survey

## TL;DR

This survey reviews the architecture, perception, decision-making systems, and recent research developments in autonomous cars, highlighting specific architectures like UFES's IARA and various research platforms.

## Contribution

It provides a comprehensive overview of autonomous car architectures, methods, and platforms, including a detailed case study of UFES's IARA system.

## Key findings

- Detailed architecture of self-driving car systems
- Review of perception and decision-making methods
- List of prominent research platforms

## Abstract

We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.