  Surround-view fisheye cameras are commonly used for near-field sensing in
automated driving scenarios, including urban driving and auto valet parking.
Four fisheye cameras, one on each side, are sufficient to cover 360{\deg}
around the vehicle capturing the entire near-field region. Based on surround
view cameras, there has been much research on parking slot detection with main
focus on the occupancy status in recent years, but little work on whether the
free slot is compatible with the mission of the ego vehicle or not. For
instance, some spots are handicap or electric vehicles accessible only. In this
paper, we tackle parking spot classification based on the surround view camera
system. We adapt the object detection neural network YOLOv4 with a novel
polygon bounding box model that is well-suited for various shaped parking
spaces, such as slanted parking slots. To the best of our knowledge, we present
the first detailed study on parking spot detection and classification on
fisheye cameras for auto valet parking scenarios. The results prove that our
proposed classification approach is effective to distinguish between regular,
electric vehicle, and handicap parking spots.
