실사고 데이터 기반 자율주행자동차 교통사고 심각도 영향요인 분석
Analysis of the Factors Influencing the Severity of Automated Vehicle Traffic Accidents Based on Actual Accident Data
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초록
비자율주행자동차 교통사고와 달리 자율주행자동차가 관여된 교통사고는 예측하기 어려운위험요소가 존재할 수 있다. 자율주행자동차 상용화 관련 규정 및 제도적 장치 마련을 위해서는 자율주행자동차 교통사고에 영향을 미치는 요인들을 분석하고 정량화하는 것이 선행되어야 한다. 따라서, 본 연구에서는 넓은 공간적 범위와 표본 수를 가진 미국 도로교통안전국의자율주행자동차 교통사고 데이터를 활용하여 교통사고 발생 현황 및 특성 분석과 순서형 로지스틱 회귀 모형 기반 심각도 영향요인 분석을 수행하였다. 교통사고 심각도 영향요인의 도출결과, 주행 거리, 교통사고 발생 전 주행 행태, 충돌 대상물이 교통사고 심각도에 영향을 미치는 것으로 나타났다. 본 연구는 실사고를 기반으로 자율주행자동차 교통사고의 심각도에 영향을 미치는 요인들을 확인했다는 점에서 의의가 있으며, 도출된 영향 요인들은 향후 자율주행자동차 교통사고 조사, 관련 규정 및 제도적 장치 마련에 활용될 수 있을 것으로 기대한다.
Abstract
Unlike non-automated vehicle accidents, traffic accidents involving automated vehicle(AV) may involve various unpredictable risk factors. Therefore, it is crucial to identify and quantify the factors affecting AV accidents to establish relevant regulations and institutional frameworks. This study utilizes AV data published by the U.S. National Highway Traffic Safety Administration, which provides a large sample size and wide spatial coverage, to analyze the occurrence and characteristics of AV accidents. Additionally, an ordinal logistic regression model is employed to assess the factors influencing accident severity. The results indicate that AV driving distance, driving behavior before the accident, and the type of collision object significantly affect accident severity. This research is meaningful as it identifies the key factors influencing the severity of AV accidents based on real-world data. The findings are expected to serve as a foundation for future investigations into AV accidents, as well as for the development of related regulations and institutional measures.
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