논문 상세보기
pp.95-108
In this study, we propose a data-driven analytical framework for systematically analyzing the driving patterns of autonomous buses and quantitatively identifying risky driving behaviors at the road-segment level using operational data from real roads. The analysis was based on Basic Safety Message (BSM) data collected over 125 days from two Panta-G autonomous buses operating in the Pangyo Autonomous Driving Testbed. Key driving indicators included speed, acceleration, yaw rate, and elevation, which were mapped onto high-definition (HD) road maps. A hybrid clustering method combining self-organizing map (SOM) and k-means++ was applied, which resulted in eight distinct driving pattern clusters. Among these, four clusters exhibited characteristics associated with risky driving such as sudden acceleration, deceleration, and abrupt steering, and were spatially visualized using digital maps. These visualizations offer practical insights for real-time monitoring and localized risk assessment in autonomous vehicle operations. The proposed framework provides empirical evidence for evaluating the operational safety and reliability of autonomous buses based on repeated behavioral patterns. Its adaptability to diverse urban environments highlights its utility for intelligent traffic control systems and future mobility policy planning.
1. 서론
2. 선행연구 고찰
2.1. 차량 운행 데이터 기반 운전행동 분석
2.2. 위험 도로구간 탐지 연구
2.3. 선행연구와의 차별성
3. 연구 방법론
3.1. 자율주행버스 데이터 수집
3.2. 데이터 전처리
3.3. 하이브리드 클러스터링 알고리즘
4. 분석결과
4.1. 하이브리드 클러스터링 알고리즘 적용 결과
4.2. 하이브리드 클러스터링 알고리즘 적용 결과
5. 결론
감사의 글
REFERENCES
