  As the popularity of autonomous vehicles has grown, many standards and
regulators, such as ISO, NHTSA, and Euro NCAP, require safety validation to
ensure a sufficient level of safety before deploying them in the real world.
Manufacturers gather a large amount of public road data for this purpose.
However, the majority of these validation activities are done manually by
humans. Furthermore, the data used to validate each driving feature may differ.
As a result, it is essential to have an efficient data selection method that
can be used flexibly and dynamically for verification and validation while also
accelerating the validation process. In this paper, we present a data selection
method that is practical, flexible, and efficient for assessment of autonomous
vehicles. Our idea is to optimize the similarity between the metadata
distribution of the selected data and a predefined metadata distribution that
is expected for validation. Our experiments on the large dataset BDD100K show
that our method can perform data selection tasks efficiently. These results
demonstrate that our methods are highly reliable and can be used to select
appropriate data for the validation of various safety functions.
