## 6 Conclusions  
It is essential to understand the environmental factors that lead to safety-critical events to identify the critical scenarios for scenario-based assessments of ADSs in urban environments. This study proposes a method to identify the critical scenario components by extracting environmental factors that trigger safety-critical events based on driver error analysis. Using a database of near-miss incidents, we extracted critical environmental factors for a scenario in which an ego vehicle turns right at a signalized intersection. The findings of this study are as follows:  
* \"Dynamic occlusion caused by oncoming right-turn vehicles\" and \"change in traffic lights\" are critical scenario components in the right-turn scenario at signalized intersections, and the proposed method can explicitly identify critical scenario components.
* The critical scenario components identified using the proposed method had challenging points for ADSs, demonstrating the usefulness of the identified components and indicating the validity of the proposed methodology.  
Identifying critical scenario components with the proposed method will enable the labeling of critical scenarios, leading to efficient safety assessments of ADSs by prioritizing the innumerable test scenarios. Moreover, the critical scenario components identified by the proposed method have the potential to set requirements for cooperative-type ADSs used in urban environments.  
This study was limited to the particular scenario of a right turn at a signalized intersection. Therefore, future plans include applying the same method to other driving scenarios to identify the critical scenario components for ADSs and to validate the proposed method further. Moreover, the proposed methodology can contribute to understanding the problems of autonomous-type systems and setting the requirements for cooperative-type systems. Therefore, we plan to use this methodology to investigate the advancement of ADSs.  
**Author Contributions:** Conceptualization, H.Y. and M.S.; methodology, H.Y. and M.S.; validation, H.Y. and M.S.; formal analysis, H.Y.; investigation, H.Y.; resources, H.Y.; data curation, H.Y.; writing--original draft preparation, H.Y.; writing--review and editing, H.Y. and M.S.; visualization, H.Y.; supervision, M.S.; project administration, M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Restrictions apply to the availability of these data. The near-miss incident database was obtained from the Smart Mobility Research Center of the Tokyo University of Agriculture and Technology and the database can be purchased from them. The naturalistic driving database was obtained from the Japan Automobile Research Institute and the database is only available for restricted organizations due to confidentiality concerns.  
**Acknowledgments:** The authors would like to express gratitude to the members of UL Systems, Inc., which has been engaged in research activities regarding the safety assessment of ADSs for many years, for their advice on the appropriateness of the method used in this paper. The authors are also grateful to the Automobile Division, Manufacturing Industries Bureau, Ministry of Economy, Trade, and Industry of Japan, and the Japan Automobile Research Institute for their cooperation in using the JARI naturalistic driving database, and the Smart Mobility Research Center of the Tokyo University of Agriculture and Technology for their cooperation in using the TUAT near-miss incident database.  
**Conflicts of Interest:** The authors declare no conflict of interest.