  ML-enabled software systems have been incorporated in many public
demonstrations for automated driving (AD) systems. Such solutions have also
been considered as a crucial approach to aim at SAE Level 5 systems, where the
passengers in such vehicles do not have to interact with the system at all
anymore. Already in 2016, Nvidia demonstrated a complete end-to-end approach
for training the complete software stack covering perception, planning and
decision making, and the actual vehicle control. While such approaches show the
great potential of such ML-enabled systems, there have also been demonstrations
where already changes to single pixels in a video frame can potentially lead to
completely different decisions with dangerous consequences. In this paper, a
structured analysis has been conducted to explore video degradation effects on
the performance of an ML-enabled pedestrian detector. Firstly, a baseline of
applying YOLO to 1,026 frames with pedestrian annotations in the KITTI Vision
Benchmark Suite has been established. Next, video degradation candidates for
each of these frames were generated using the leading video codecs libx264,
libx265, Nvidia HEVC, and AV1: 52 frames for the various compression presets
for color and gray-scale frames resulting in 104 degradation candidates per
original KITTI frame and 426,816 images in total. YOLO was applied to each
image to compute the intersection-over-union (IoU) metric to compare the
performance with the original baseline. While aggressively lossy compression
settings result in significant performance drops as expected, it was also
observed that some configurations actually result in slightly better IoU
results compared to the baseline. The findings show that carefully chosen lossy
video configurations preserve a decent performance of particular ML-enabled
systems while allowing for substantial savings when storing or transmitting
data.
