  Machine learning (ML)-enabled approaches are considered a substantial support
technique of detection and classification of obstacles of traffic participants
in self-driving vehicles. Major breakthroughs have been demonstrated the past
few years, even covering complete end-to-end data processing chain from sensory
inputs through perception and planning to vehicle control of acceleration,
breaking and steering. YOLO (you-only-look-once) is a state-of-the-art
perception neural network (NN) architecture providing object detection and
classification through bounding box estimations on camera images. As the NN is
trained on well annotated images, in this paper we study the variations of
confidence levels from the NN when tested on hand-crafted occlusion added to a
test set. We compare regular pedestrian detection to upper and lower body
detection. Our findings show that the two NN using only partial information
perform similarly well like the NN for the full body when the full body NN's
performance is 0.75 or better. Furthermore and as expected, the network, which
is only trained on the lower half body is least prone to disturbances from
occlusions of the upper half and vice versa.
