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)、网络等许多领域进步的顶点。本文件将介绍读者使用自主车辆的制造技术,重点介绍用于自主车辆开发的开放源工具和图书馆,使开发商和研究人员更便宜、更容易地参与实地开发。所涵盖的议题如下:首先,我们将讨论自主车辆使用的传感器,并总结其在不同环境、成本和独特特点的性能。然后,我们将涵盖同步本地化和绘图(SLAM)以及每种模式的算法。第三,我们将审查大众开源驱动模拟器,这是培训机器学习模型和测试车辆软件性能的具有成本效益的方法。然后,我们将在选择一个时突出嵌入的操作系统以及安全和发展考虑。之后,我们将讨论车辆对车辆的到飞行器(V2V)和自动飞行器(IoV)的通信,这是将自动车辆联网技术与自主车辆连接起来以扩大其功能的领域。我们将审查车辆自动化、商业和开源驱动器的模拟器的五级模拟器,这是培训机器学习模式的成本效益高端操作系统,最后,并将介绍其主要制造业的实地投资工具。