  In order to ensure autonomous vehicles are safe for on-road deployment,
simulation-based testing has become an integral complement to on-road testing.
The rise in simulation testing and validation reflects a growing need to verify
that AV behavior is consistent with desired outcomes even in edge case
scenarios $-$ which may seldom or never appear in on-road testing data. This
raises a critical question: to what extent are AV failures in simulation
consistent with data collected from real-world testing? As a result of the gap
between simulated and real sensor data (sim-to-real gap), failures in
simulation can either be spurious (simulation- or simulator-specific issues) or
relevant (safety-critical AV system issues). One possible method for validating
if simulated time series failures are consistent with real world time series
sensor data could involve retrieving instances of the failure scenario from a
real-world time series dataset, in order to understand AV performance in these
scenarios. Adopting this strategy, we propose a formal definition of what
constitutes a match between a real-world labeled time series data item and a
simulated scenario written from a fragment of the Scenic probabilistic
programming language for simulation generation. With this definition of a
match, we develop a querying algorithm that identifies the subset of a labeled
time series dataset matching a given scenario. To allow this approach to be
used to verify the safety of other cyber-physical systems (CPS), we present a
definition and algorithm for matching scalable beyond the autonomous vehicles
domain. Experiments demonstrate the precision and scalability of the algorithm
for a set of challenging and uncommon time series scenarios identified from the
nuScenes autonomous driving dataset. We include a full system implementation of
the querying algorithm freely available for use across a wide range of CPS.
