  Systematically testing models learned from neural networks remains a crucial
unsolved barrier to successfully justify safety for autonomous vehicles
engineered using data-driven approach. We propose quantitative k-projection
coverage as a metric to mediate combinatorial explosion while guiding the data
sampling process. By assuming that domain experts propose largely independent
environment conditions and by associating elements in each condition with
weights, the product of these conditions forms scenarios, and one may interpret
weights associated with each equivalence class as relative importance.
Achieving full k-projection coverage requires that the data set, when being
projected to the hyperplane formed by arbitrarily selected k-conditions, covers
each class with number of data points no less than the associated weight. For
the general case where scenario composition is constrained by rules, precisely
computing k-projection coverage remains in NP. In terms of finding minimum test
cases to achieve full coverage, we present theoretical complexity for important
sub-cases and an encoding to 0-1 integer programming. We have implemented a
research prototype that generates test cases for a visual object defection unit
in automated driving, demonstrating the technological feasibility of our
proposed coverage criterion.
