  Evaluating the performance of software for automated vehicles is
predominantly driven by data collected from the real world. While professional
test drivers are supported with technical means to semi-automatically annotate
driving maneuvers to allow better event identification, simple data loggers in
large vehicle fleets typically lack automatic and detailed event classification
and hence, extra effort is needed when post-processing such data. Yet, the data
quality from professional test drivers is apparently higher than the one from
large fleets where labels are missing, but the non-annotated data set from
large vehicle fleets is much more representative for typical, realistic driving
scenarios to be handled by automated vehicles. However, while growing the data
from large fleets is relatively simple, adding valuable annotations during
post-processing has become increasingly expensive. In this paper, we leverage
Z-order space-filling curves to systematically reduce data dimensionality while
preserving domain-specific data properties, which allows us to explore even
large-scale field data sets to spot interesting events orders of magnitude
faster than processing time-series data directly. Furthermore, the proposed
concept is based on an analytical approach, which preserves explainability for
the identified events.
