Autonomous vehicle (AV) accidents introduce uncertainty in liability attribution, as responsibility is divided between humans and automated systems. The 2018 Arizona crash highlighted growing societal concerns about accountability. To address these issues, prior studies proposed investigation processes considering perception sensors, driving control systems, communication infrastructure, and cybersecurity. However, conducting such investigations requires integrating large-scale data from multiple sources, including vehicle sensors, onboard recorders, V2X communications, and road infrastructure. Raw data often lack descriptive information, limiting their use in real investigations. This study establishes a structured mapping framework linking investigation procedures, responsible entities, items, and data across accident phases. With this backdrop, an autonomous driving–specific metadata schema extending DCAT was designed, comprising 10 Classes and 76 Properties. To demonstrate its applicability, a prototype data catalog user interface (UI) was conceptualized with data discovery and visualization examples. The proposed schema strengthens accountability and interoperability by explicitly aligning responsibilities and data relationships. It enables precise event localization and effective linkage of heterogeneous data. Future work will refine the schema by incorporating DSSAD, V2X, and security log data, and develop a user-tested UI prototype as a practical support tool for AV accident investigation.