  In this paper, we consider the imperfection within machine learning-based 2D
object detection and its impact on safety. We address a special sub-type of
performance limitations: the prediction bounding box cannot be perfectly
aligned with the ground truth, but the computed Intersection-over-Union metric
is always larger than a given threshold. Under such type of performance
limitation, we formally prove the minimum required bounding box enlargement
factor to cover the ground truth. We then demonstrate that the factor can be
mathematically adjusted to a smaller value, provided that the motion planner
takes a fixed-length buffer in making its decisions. Finally, observing the
difference between an empirically measured enlargement factor and our formally
derived worst-case enlargement factor offers an interesting connection between
the quantitative evidence (demonstrated by statistics) and the qualitative
evidence (demonstrated by worst-case analysis).
