  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.
