scenario.center: Methods from Real-world Data to a Scenario Database
Michael Schuldes, Christoph Glasmacher, Lutz Eckstein
TL;DR
The paper addresses the challenge of validating automated driving systems in complex urban environments by proposing a scenario database framework, scenario.center, that automatically extracts scenarios from real-world data, unifies them into a common parameter space, and provides querying and generation of executable scenarios. It introduces a six-layer scenario concept, an OMEGA-format input interface, and a graph-based query system, plus multiple generation modes (RtS/ARtS) and parametrized sampling to cover the scenario space. The authors demonstrate the approach using the urban inD dataset, showing large numbers of enveloping and base scenarios and providing OpenSCENARIO/OpenDRIVE outputs and visualizations; they also compare scenario.center with existing databases and discuss urban applicability and data-quality considerations. The work advances scenario-based testing by enabling scalable, urban-capable scenario management with automated extraction, rich querying of sequences, and executable scenario generation, with public accessibility and plans for federated interoperability.
Abstract
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 .