With the implementation of the new EU regulation 2022/1426 regarding the type-approval of the automated driving system (ADS) of fully automated vehicles, scenario-based testing has gained significant importance in evaluating the performance and safety of advanced driver assistance systems and automated driving systems. However, the exploration and generation of concrete scenarios from a single logical scenario can often lead to a number of similar or redundant scenarios, which may not contribute to the testing goals. This paper focuses on the the goal to reduce the scenario set by clustering concrete scenarios from a single logical scenario. By employing clustering techniques, redundant and uninteresting scenarios can be identified and eliminated, resulting in a representative scenario set. This reduction allows for a more focused and efficient testing process, enabling the allocation of resources to the most relevant and critical scenarios. Furthermore, the identified clusters can provide valuable insights into the scenario space, revealing patterns and potential problems with the system's behavior.

翻译：随着欧盟新法规2022/1426关于全自动驾驶车辆自动驾驶系统（ADS）型式认证的实施，基于场景的测试在评估先进驾驶辅助系统与自动驾驶系统性能及安全性方面的重要性显著提升。然而，从单一逻辑场景探索和生成具体场景时，常会产生大量相似或冗余场景，这些场景可能对测试目标无益。本文聚焦于通过聚类方法对单一逻辑场景生成的具体场景进行约简，从而缩减场景集的目标。通过应用聚类技术，可识别并剔除冗余及无趣场景，最终获得具有代表性的场景集。该约简过程能够实现更具针对性和高效的测试流程，使资源得以集中配置于最关键与最关键的场景。此外，识别出的聚类结果可为场景空间提供宝贵洞见，揭示系统行为中的模式与潜在问题。