{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:34:19Z","timestamp":1768509259622,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information Communications Technology Planning Evaluatio","award":["IITP-2025-RS-2020-II201741"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201741"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical testing costly and limited in scope, particularly for rare and safety-critical scenarios. To address this, simulation has become a core component in validation by providing scalable, controllable, and repeatable testing environments. This study proposes a trajectory-based scenario classification framework that emphasizes both generality and interpretability. Specifically, we define a set of rule-based maneuver classification criteria using lateral acceleration patterns and apply them to simulated urban driving scenarios modeled with OpenSCENARIO. To address overlapping maneuver characteristics, a priority ordering of classification rules is introduced to resolve ambiguities. The proposed method was evaluated on a dataset comprising 7 types of maneuvers, including straight driving, lane changes, turns, roundabouts, and U-turns. Experimental results demonstrate the effectiveness of rule-driven classification based on vehicle trajectory dynamics and highlight the potential of this approach for structured scenario definition and validation in ADS simulation environments.<\/jats:p>","DOI":"10.3390\/ijgi15010037","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:20:37Z","timestamp":1768206037000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rule-Based Scenario Classification Using Vehicle Trajectories"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8078-6892","authenticated-orcid":false,"given":"Sungmo","family":"Ku","sequence":"first","affiliation":[{"name":"Department of Smart Factory Engineering, Tech University of Korea, Siheung 15073, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6198-3168","authenticated-orcid":false,"given":"Jinho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Tech University of Korea, Siheung 15073, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7572","DOI":"10.1109\/JIOT.2021.3130054","article-title":"Autonomous Driving Security: State of the Art and Challenges","volume":"9","author":"Gao","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012096","DOI":"10.1088\/1757-899X\/252\/1\/012096","article-title":"Current Challenges in Autonomous Driving","volume":"252","author":"Molea","year":"2017","journal-title":"IOP Conf. 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