C-ITS 기반 PVD를 활용한 화물차량의 고속도로 주행 시 전방충돌경보 상황에 대한 특성 분석

서지정보

ㆍ발행기관 : 한국도로학회 ㆍ수록지정보 : 한국도로학회논문집 / 25권 / 6호
ㆍ저자명 : 김동민, 이환필, 이주영

목차

1. 서론
2. 선행 연구 고찰
3. 연구방법론
4. 데이터 개요
4.1. 위험물·화물차량 PVD
4.2. 경부선 사고 및 노변기지국 관련 데이터
5. 클러스터링 기반 FCW 분석데이터셋 구축
5.1. 차량 주행 상태에 따른 전방충돌경보 상황 군집화
5.2. FCW 관련 차량 상태정보와 사고분석 정보를 병합한분석데이터 가공
6. 전방충돌경보의 차량 주행 상태와 고속도로 내사고위험상황에 대한 분석
7. 결론
감사의 글

영어 초록

PURPOSES : This study aims to perform a quantitative analysis of Forward Collision Warning and crash frequency using heavy vehicle driving data collected in expressway driving environments, and to classify the driving environments where Forward Collision Warnings of heavy vehicles occur for accident-prone areas and analyze their occurrence characteristics. METHODS : A bivariate Gaussian mixture model based on inter-vehicle distance gap and speed-acceleration parameters is used to classify the environment in which Forward Collision Warning occurs for heavy vehicles driving on expressways. For this analysis, Probe Vehicle Data of 80 large trucks collected by C-ITS devices of Korea Expressway Corporation from May to June 2022. Combined with accident information from the past five years, a detailed analysis of the classified driving environments is conducted. RESULTS : The results of the clustering analysis categorizes Forward Collision Warning environments into three groups: Group I (highdensity, high-speed), Group II (high-density, low-speed), and Group III (low-density, high-speed). It reveals a positive correlation between Forward Collision Warning frequency and accident rates at these points, with Group I prevailing. Road characteristics at sites with different accident incidences showed that on-ramps and toll gates had high occurrences of both accidents and warnings. Furthermore, acceleration deviation at high-accident sites was significant across all groups, with variable speed deviations noted for each warning group. CONCLUSIONS : The Forward Collision Warning of heavy vehicles on expressways is classified into three types depending on the driving environment, and the results of these environmental classifications can be used as a basis for building a road environment that reduces the risk of crashes for heavy vehicles.

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