Standardizing Scenario Annotations: a Comprehensive Mapping and Gap Analysis Across Openscenario, Openodd, and Openlabel

Although the objectives of ASAM's OpenODD, OpenLABEL, and OpenSCENARIO standards are complementary and intended to be used together, practitioners face difficulties in smoothly and consistently aligning scenario definitions (OpenSCENARIO), annotation ontologies (OpenLABEL), and operational constraints (OpenODD) due to the lack of comprehensive mappings among these standards. To address this challenge, this paper systematically maps scenario elements from OpenSCENARIO to OpenODD attributes and OpenLABEL tags for typical autonomous driving use cases. This systematic mapping helps create a unified framework that clearly illustrates both their potential synergy and certain gaps that were previously unnoticed. Through detailed comparison, we identify mismatches in action granularity, missing tags for scenario contexts, and environmental constraints that are either overly general or insufficiently addressed across these three standards. These findings highlight the need for strategic extensions and refinements in the taxonomy of each standard. Additionally, these findings especially support the ongoing development and maturation of the OpenODD standard. By clearly identifying areas of adequate coverage and incomplete aspects, we provide a practical roadmap for scenario developers, data engineers, and system integrators to achieve consistent, reliable, and interoperable autonomous driving testing and validation.