Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround view cameras typically comprise of four fisheye cameras with 190{\deg} field-of-view covering the entire 360{\deg} around the vehicle focused on near field sensing. They are the principal sensor for low-speed, high accuracy and close-range sensing applications, such as automated parking, traffic jam assistance and low-speed emergency braking. In this work, we describe our visual perception architecture on surround view cameras designed for a system deployed in commercial vehicles, provide a functional review of the different stages of such a computer vision system, and discuss some of the current technological challenges. We have designed our system into four modular components namely Recognition, Reconstruction, Relocalization and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and how they are synergized to form a complete system. Qualitative results are presented in the video at \url{https://youtu.be/ae8bCOF77uY}.
翻译:相机是自动驾驶系统中的主要传感器。它们提供高信息密度,是探测人类视觉所设定的道路基础设施提示的最佳方法。光学摄像机通常由四台直观摄像机组成,其范围为190=deg}视野范围覆盖车辆周围以近地遥感为重点的整个360×deg},它们是低速、高精度和近距离遥感应用的主要传感器,如自动停车、交通阻塞协助和低速紧急制动。在这项工作中,我们描述了我们为商用车辆中安装的系统设计的环绕视图摄像机的视觉结构,对计算机视觉系统的不同阶段进行了功能性审查,并讨论了当前的一些技术挑战。我们已经将我们的系统设计成四个模块组件,即识别、重建、重新定位和重组。我们共同称之为4R结构。我们讨论了每个部件如何实现一个具体方面,以及如何合成一个完整的系统。在\url{https://youtu.be/ae8bCO77UY}的视频中展示了质量结果。