Ensuring validation for highly automated driving poses significant obstacles to the widespread adoption of highly automated vehicles. Scenario-based testing offers a potential solution by reducing the homologation effort required for these systems. However, a crucial prerequisite, yet unresolved, is the definition and reduction of the test space to a finite number of scenarios. To tackle this challenge, we propose an extension to a contrastive learning approach utilizing graphs to construct a meaningful embedding space. Our approach demonstrates the continuous mapping of scenes using scene-specific features and the formation of thematically similar clusters based on the resulting embeddings. Based on the found clusters, similar scenes could be identified in the subsequent test process, which can lead to a reduction in redundant test runs.

翻译：确保高度自动化驾驶的验证对高度自动化车辆的广泛应用构成了重大障碍。基于场景的测试通过减少这些系统所需的认证工作量提供了一种潜在解决方案。然而，一个关键的前提条件——测试空间的定义及其缩减至有限数量场景——至今尚未解决。为应对这一挑战，我们提出了一种基于图的对比学习方法的扩展，以构建有意义的嵌入空间。我们的方法展示了利用场景特定特征对场景进行连续映射，并根据生成的嵌入形成主题相似的聚类。基于所发现的聚类，在后续测试过程中可以识别出相似场景，从而减少冗余测试次数。