  Background/Context. The use of automated driving systems (ADSs) in the real
world requires rigorous testing to ensure safety. To increase trust, ADSs
should be tested on a large set of diverse road scenarios. Literature suggests
that if a vehicle is driven along a set of geometrically diverse roads-measured
using various diversity measures (DMs)-it will react in a wide range of
behaviours, thereby increasing the chances of observing failures (if any), or
strengthening the confidence in its safety, if no failures are observed. To the
best of our knowledge, however, this assumption has never been tested before,
nor have road DMs been assessed for their properties. Objective/Aim. Our goal
is to perform an exploratory study on 47 currently used and new, potentially
promising road DMs. Specifically, our research questions look into the road DMs
themselves, to analyse their properties (e.g. monotonicity, computation
efficiency), and to test correlation between DMs. Furthermore, we look at the
use of road DMs to investigate whether the assumption that diverse test suites
of roads expose diverse driving behaviour holds. Method. Our empirical analysis
relies on a state-of-the-art, open-source ADSs testing infrastructure and uses
a data set containing over 97,000 individual road geometries and matching
simulation data that were collected using two driving agents. By sampling
random test suites of various sizes and measuring their roads' geometric
diversity, we study road DMs properties, the correlation between road DMs, and
the correlation between road DMs and the observed behaviour.
