Autonomous vehicles are the culmination of advances in many areas such as sensor technologies, artificial intelligence (AI), networking, and more. This paper will introduce the reader to the technologies that build autonomous vehicles. It will focus on open-source tools and libraries for autonomous vehicle development, making it cheaper and easier for developers and researchers to participate in the field. The topics covered are as follows. First, we will discuss the sensors used in autonomous vehicles and summarize their performance in different environments, costs, and unique features. Then we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for each modality. Third, we will review popular open-source driving simulators, a cost-effective way to train machine learning models and test vehicle software performance. We will then highlight embedded operating systems and the security and development considerations when choosing one. After that, we will discuss Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are areas that fuse networking technologies with autonomous vehicles to extend their functionality. We will then review the five levels of vehicle automation, commercial and open-source Advanced Driving Assistance Systems, and their features. Finally, we will touch on the major manufacturing and software companies involved in the field, their investments, and their partnerships. These topics will give the reader an understanding of the industry, its technologies, active research, and the tools available for developers to build autonomous vehicles.

翻译：自主车辆是传感器技术、人工智能（AI）和网络等多个领域技术进步的集大成者。本文将向读者介绍构建自主车辆所需的技术，重点关注用于自主车辆开发的开源工具与库，从而降低开发人员和研究人员参与该领域的门槛与成本。涵盖主题如下：首先，讨论自主车辆中使用的传感器，并总结它们在不同环境中的性能、成本和独特特征；其次，介绍同时定位与地图构建（SLAM）及各模态对应的算法；第三，回顾主流的开源驾驶模拟器——一种用于训练机器学习模型和测试车辆软件性能的高性价比方式；随后，重点阐述嵌入式操作系统及其选型时的安全与开发考量；接着，讨论车对车（V2V）和车联网（IoV）通信——这些领域将网络技术与自主车辆融合以扩展其功能；之后，回顾车辆自动化的五个等级、商用及开源高级驾驶辅助系统及其功能特点；最后，简要介绍参与该领域的主要制造及软件公司、它们的投资与合作伙伴关系。上述内容将使读者全面了解该行业、其技术、活跃研究方向以及开发者构建自主车辆可用的工具。