Scenario-based testing is a promising method to develop, verify and validate automated driving systems (ADS) since pure on-road testing seems inefficient for complex traffic environments. A major challenge for this approach is the provision and management of a sufficient number of scenarios to test a system. The provision, generation, and management of scenario at scale is investigated in current research. This paper presents the scenario database scenario.center ( https://scenario.center ) to process and manage scenario data covering the needs of scenario-based testing approaches comprehensively and automatically. Thereby, requirements for such databases are described. Based on those, a four-step approach is proposed. Firstly, a common input format with defined quality requirements is defined. This is utilized for detecting events and base scenarios automatically. Furthermore, methods for searchability, evaluation of data quality and different scenario generation methods are proposed to allow a broad applicability serving different needs. For evaluation, the methodology is compared to state-of-the-art scenario databases. Finally, the application and capabilities of the database are shown by applying the methodology to the inD dataset. A public demonstration of the database interface is provided at https://scenario.center .

翻译：基于场景的测试是一种有前景的方法，用于开发、验证和确认自动驾驶系统（ADS），因为纯道路测试在复杂交通环境中似乎效率低下。该方法面临的主要挑战是如何提供和管理足够数量的场景来测试系统。当前研究关注大规模场景的提供、生成和管理。本文介绍了场景数据库scenario.center（https://scenario.center），旨在全面自动地处理和管理满足基于场景测试方法需求的场景数据。为此，本文描述了此类数据库的要求，并基于这些要求提出了一个四步方法。首先，定义了具有明确质量要求的通用输入格式，用于自动检测事件和基础场景。此外，提出了可搜索性、数据质量评估以及不同场景生成方法，以实现广泛适用性并满足多样化需求。在评估方面，将该方法与最新场景数据库进行了比较。最后，通过将方法应用于inD数据集展示了数据库的应用与能力。数据库界面的公开演示详见https://scenario.center。