실도로 데이터를 활용한 교차로 유형별 자율주행 보수성 평가 연구
Evaluation of Autonomous Driving Conservativeness by Urban Intersections with Real-World Data
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초록
혼합교통류 환경에서 자율차의 보수적인 주행행태는 전체 교통흐름에 부정적인 영향을 줄수 있다. 선제적으로 혼합교통류의 이동성과 안전성을 관리하기 위해서는 실도로에서 수집된자율주행 데이터를 활용하여 주행행태를 과학적 평가하는 것이 필수적이다. 본 연구에서는Waymo Open Dataset을 활용하여 단속류 교차로 유형별 자율차와 비자율차의 주행행태를 비교평가하는 방법론을 제안하였다. 영상자료를 통해 도로구간을 구분하고, 차량 추종 상황에서 자율차와 비자율차의 Time-to Collision(TTC)를 기반으로 상충률 차이를 비교하는 자율주행 보수성 지표(Autonomous Driving Conservativeness Index, ADCI)를 고안하였다. 분석 결과 모든 TTC 임계값에서 4지 비신호 교차로가 높은 ADCI로 관찰되었다. 이는 교차로 유형 중 4지 비신호교차로에서 자율차의 주행 보수성(Conservativeness)이 높아, 비자율차 대비 보수적이게 주행함을 의미한다. 장기간 지속될 혼합교통류 환경에서 자율차가 전체 교통흐름에 부정적인 영향을주지 않기 위해 도로구간별 주행행태를 분석하고 지원방안을 도출할 필요함을 시사한다. 본연구의 방법론은 실도로 데이터셋 기반 자율차에 영향을 미치는 요인들을 분석하는 기초 자료로 활용될 것으로 기대된다.
Abstract
In mixed traffic conditions, the conservative driving behavior of autonomous vehicles (AV) would negatively affect overall traffic performance. In order to manage mobility and safety in mixed traffic conditions, it is essential to scientifically evaluate driving behavior using autonomous driving data collected from real-world. This study proposed a methodology to evaluate the driving behavior of autonomous vehicles (AV) and manual vehicles (MV) at different types of intersections using the Waymo Open Dataset. Urban street were identified through video data, and the autonomous driving conservativeness index (ADCI) was devised to compare the difference in time-to-collision (TTC) based conflict rates between AV and MV in car following situations. The results showed that unsignalized 4-way intersections had the highest ADCI value, indicating greater conservativeness in driving behavior. This indicates the necessity of analyzing the driving behavior of each road section and deriving support measures to prevent AV from negatively affecting the overall traffic performance in mixed traffic conditions. The methodology of this study is expected to serve as foundational for analyzing factors affecting AV using real-world datasets.
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