  Scenario-based testing is envisioned as a key approach for the safety
assurance of autonomous vehicles. In scenario-based testing, relevant (driving)
scenarios are the basis of tests. Many recent works focus on specification,
variation, generation and execution of individual scenarios. In this work, we
address the open challenges of classifying sets of scenarios and measuring
coverage of theses scenarios in recorded test drives. Technically, we define
logic-based classifiers that compute features of scenarios on complex data
streams and combine these classifiers into feature trees that describe sets of
scenarios. We demonstrate the expressiveness and effectiveness of our approach
by defining a scenario classifier for urban driving and evaluating it on data
recorded from simulations.
