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.

翻译：基于情景的测试被视为自动驾驶车辆安全保证的关键方法。在基于情景的测试中，相关（驾驶）情景构成测试的基础。近期大量工作聚焦于单个情景的规范、变体、生成与执行。本研究针对情景集合分类及记录测试驾驶中情景覆盖度测量的开放挑战，提出了一种技术方案：定义基于逻辑的分类器以计算复杂数据流上的情景特征，并将这些分类器组合成描述情景集合的特征树。通过定义城市驾驶情景分类器并在仿真记录数据上进行评估，我们验证了该方法的表现力与有效性。