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Scenario-based testing is envisioned as a key approach for the safetyassurance 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, weaddress the open challenges of classifying sets of scenarios and measuringcoverage of theses scenarios in recorded test drives. Technically, we definelogic-based classifiers that compute features of scenarios on complex datastreams and combine these classifiers into feature trees that describe sets ofscenarios. We demonstrate the expressiveness and effectiveness of our approachby defining a scenario classifier for urban driving and evaluating it on datarecorded from simulations.

Tree-Based Scenario Classification: A Formal Framework for Coverage Analysis on Test Drives of Autonomous Vehicles