Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360{\deg} around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm and partial visibility of objects. Due to the large radial distortion of fisheye cameras, standard algorithms cannot be extended easily to the surround-view use case. Thus, we are motivated to provide a self-contained reference for automotive fisheye camera perception for researchers and practitioners. Firstly, we provide a unified and taxonomic treatment of commonly used fisheye camera models. Secondly, we discuss various perception tasks and existing literature. Finally, we discuss the challenges and future direction.

翻译：在自动驾驶中,近距离摄像头通常用于近距离感测。四边的四边的鱼眼照相机足以覆盖整个近地区域的车辆周围360×deg}。有些主要使用案例是自动停车、交通阻塞协助和城市驾驶。由于汽车视像的焦点是远地视像,因此,数据集有限,近地视像任务方面的工作很少。与远地视像相比,近地视像对10厘米和部分可见物体的高度精确物体探测要求构成额外挑战。由于鱼眼摄像机的大规模辐射扭曲,标准算法无法轻易扩大到环地使用。因此,我们有志于为研究人员和从业人员提供自成一体的自成一体的鱼眼相机感知。首先,我们对常用的鱼眼照相机模型提供统一和分类处理。第二,我们讨论各种视觉任务和现有文献。最后,我们讨论挑战和未来方向。