Tree-Based Scenario Classification

Scenario-based testing is envisioned as a key approach for the safety assurance of automated driving systems. 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 recorded test drives into such scenarios and measuring scenario coverage in these test drives. Technically, we specify features in logic formulas over complex data streams and construct tree-based classifiers for scenarios from these feature specifications. For such specifications, we introduce CMFTBL, a new logic that extends existing linear-time temporal logics with aspects that are essential for concise specifications that work on field-recorded data. We demonstrate the expressiveness and effectiveness of our approach by defining a family of related scenario classifiers for different aspects of urban driving.