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 camera systems 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 sensors 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 provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed 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 provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.

翻译：相机是自动驾驶系统的主要传感器。它们提供高信息密度，并最适于检测为人类视觉设计的道路基础设施标识。环视相机系统通常由四只视场角超过190°的鱼眼相机组成，覆盖车辆周围360°范围，专注于近场感知。它们是低速、高精度、近距离传感应用（如自动泊车、交通拥堵辅助和低速紧急制动）的核心传感器。本文对此类视觉系统进行了详细综述，基于可分解为四个模块化组件（即识别、重建、重定位与重组）的架构框架展开论述，并联合称之为“4R架构”。我们阐释了每个组件如何实现特定功能，并提出协同整合可形成完整低速自动化感知系统的论点。通过既往研究成果及此类系统的架构设计方案，我们为该论点提供了支撑。定性结果展示于视频https://youtu.be/ae8bCOF77uY中。