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：随着欧盟关于全自动驾驶汽车自动驱动系统（ADS）型式认证的新法规2022/1426的实施，基于场景的测试在评估高级驾驶辅助系统和自动驾驶系统的性能与安全性方面变得日益重要。然而，从单一逻辑场景中探索和生成具体场景时，常常会产生大量相似或冗余的场景，这些场景可能对测试目标无益。本文聚焦于通过聚类技术从单一逻辑场景中精简场景集的目标。通过运用聚类方法，可以识别并剔除冗余和无关紧要的场景，从而得到具有代表性的场景集。这种精简使得测试过程更加聚焦和高效，能够将资源分配给最相关和最关键的场景。此外，识别出的聚类结果可为场景空间提供宝贵见解，揭示系统行为模式及潜在问题。