  The MUSICC project has created a proof-of-concept scenario database to be
used as part of a type approval process for the verification of automated
driving systems (ADS). This process must include a highly automated means of
evaluating test results, as manual review at the scale required is impractical.
  This paper sets out a framework for assessing an ADS's behavioural safety in
normal operation (i.e. performance of the dynamic driving task without
component failures or malicious actions). Five top-level evaluation criteria
for ADS performance are identified. Implementing these requires two types of
outcome scoring rule: prescriptive (measurable rules which must always be
followed) and risk-based (undesirable outcomes which must not occur too often).
Scoring rules are defined in a programming language and will be stored as part
of the scenario description.
  Risk-based rules cannot give a pass/fail decision from a single test case.
Instead, a framework is defined to reach a decision for each functional
scenario (set of test cases with common features). This considers statistical
performance across many individual tests. Implications of this framework for
hypothesis testing and scenario selection are identified.
