In this paper, we provide a survey on automotive surround-view fisheye
optics, with an emphasis on the impact of optical artifacts on computer vision
tasks in autonomous driving and ADAS. The automotive industry has advanced in
applying state-of-the-art computer vision to enhance road safety and provide
automated driving functionality. When using camera systems on vehicles, there
is a particular need for a wide field of view to capture the entire vehicle's
surroundings, in areas such as low-speed maneuvering, automated parking, and
cocoon sensing. However, one crucial challenge in surround-view cameras is the
strong optical aberrations of the fisheye camera, which is an area that has
received little attention in the literature. Additionally, a comprehensive
dataset is needed for testing safety-critical scenarios in vehicle automation.
The industry has turned to simulation as a cost-effective strategy for creating
synthetic datasets with surround-view camera imagery. We examine different
simulation methods (such as model-driven and data-driven simulations) and
discuss the simulators' ability (or lack thereof) to model real-world optical
performance. Overall, this paper highlights the optical aberrations in
automotive fisheye datasets, and the limitations of optical reality in
simulated fisheye datasets, with a focus on computer vision in surround-view
optical systems.

本文提供了关于汽车环视鱼眼光学的调查，重点关注光学伪像对自动驾驶和 ADAS 中的计算机视觉任务的影响。通过分析不同的模拟方法，讨论了模拟器对真实光学性能的建模能力（或缺乏）以及汽车鱼眼数据集中的光学像差和模拟鱼眼数据集的限制，突出了计算机视觉和环视光学系统中光学伪像的重要性。