교통사고자료 분석을 통한 운전자 교육용 주행 시뮬레이터 시나리오 선정체계
Selection Framework of Driving Simulator Scenarios for Driver Education Based on Traffic Accident Data Analysis
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초록
본 연구에서는 주행 시뮬레이터를 활용한 운전자 교육 효과가 극대화될 수 있는 시뮬레이터 시나리오를 구성할 수 있도록 교통사고자료를 분석하여 교통사고 유발 가능성이 높은 고위험 운전자 그룹이 일반 운전자 그룹에 비해 사고 발생 빈도가 높은 교통상황을 선정하는 체계를 제시하였다. 주행 시뮬레이터를 활용한 운전자 교육을 위해 교통사고자료를 구축하고 이를 이용하여 교육대상이 될 고위험군 운전자를 선정하는 한편, 기존의 결과위주의 분류로 기록된 교통사고자료의 사고 유형을 운전 당사자의 행동 중심으로 세분화하여 교육대상별로 이를 분석하였다. 교통사고자료를 활용하여 각각 빈도 분석, 모비율 검정 등의 방법으로 공통적이고 차별적인 사고유형을 도출하고, 도출된 대상별 위험운전항목 그리고 운전자 특성(연령대)의 연관관계를 살피고자 하였다. 본 분석은 연구 목적에 맞게 제한적으로 분류한 고위험군 운전자를 대상으로 하고 있으며, 분석을 위해 추출한 표본의 크기와 대상이 제한적이었기 때문에 향후 연구에서는 이를 보완하여야 할 것이다.
Abstract
This paper proposes a framework for selecting driving simulator scenarios for high risk driver’s education based on traffic accident data analysis by comparing driving behaviors of high risk drivers with those of general drivers. Based on previous research, novice and elderly drivers were selected as the High Risk Drivers Group. Traffic accident data from the police database was utilized to select risky driving scenarios in order to educate drivers who were deemed more likely to drive unsafely. A newly subdivided categorization recorded in the database consists of vehicle to vehicle collisions was developed and is more versatile than the previous categorization system which consisted of only four categories of collisions. Statistical analyses, including frequency analysis and population ratio tests to differentiate the high risk groups, were conducted and the polynomial regression model was developed to verify the relationship between ages and specific traffic accident situations. As this paper used a limited data set, future research may extend the scope of the analysis.
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