As the number of autonomous vehicles increases, the number of accidents also increases every year. These incidents include general car/traffic accidents and may introduce new potential issues such as cybersecurity and sensor errors of autonomous vehicles. The existing traffic accident investigation method has limitations in identifying the cause of the autonomous vehicle accident. Some states in the US (e.g., California and Texas) introduced limited items of Lv. 2 autonomous vehicle accidents. For instance, “vehicle level” and “autonomous mode/conventional mode” are being investigated to identify the cause of autonomous vehicle accidents. Therefore, it is crucial to propose accident investigation items and procedures in preparation for various autonomous vehicles that may occur in the future. In order to address these issues, this study collected reports used in existing traffic accident investigations, autonomous driving‐related reports and literature, and accident videos involving autonomous driving to build investigation items. First, we reviewed the items required for investigation in the event of a conventional vehicle accident and added additional investigation items deemed necessary to be reviewed in addition to the existing reports. Second, based on the conventional vehicle accident investigation items, this study derived the autonomous driving traffic accident investigation items. Finally, an accident involving autonomous vehicle(s) investigation process was established that can be used by the police and various investigation jurisdictions. The results of this paper can improve the understanding of the cause of future traffic accidents involving autonomous vehicles.

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.