  Accurate environment perception is essential for automated driving. When
using monocular cameras, the distance estimation of elements in the environment
poses a major challenge. Distances can be more easily estimated when the camera
perspective is transformed to a bird's eye view (BEV). For flat surfaces,
Inverse Perspective Mapping (IPM) can accurately transform images to a BEV.
Three-dimensional objects such as vehicles and vulnerable road users are
distorted by this transformation making it difficult to estimate their position
relative to the sensor. This paper describes a methodology to obtain a
corrected 360{\deg} BEV image given images from multiple vehicle-mounted
cameras. The corrected BEV image is segmented into semantic classes and
includes a prediction of occluded areas. The neural network approach does not
rely on manually labeled data, but is trained on a synthetic dataset in such a
way that it generalizes well to real-world data. By using semantically
segmented images as input, we reduce the reality gap between simulated and
real-world data and are able to show that our method can be successfully
applied in the real world. Extensive experiments conducted on the synthetic
data demonstrate the superiority of our approach compared to IPM. Source code
and datasets are available at https://github.com/ika-rwth-aachen/Cam2BEV
