한국ITS학회 논문지 (The Journal of The Korea Institute of Intelligent Transport Systems)

  • 제17권2호
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  • Pages.128-141
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  • 2018
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  • 1738-0774(pISSN)
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  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
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협력형 차량 안전 시스템의 잠재적 안전 효과 분석 연구

Potential Safety Benefit Analysis of Cooperative Driver Assistance Systems Via Vehicle-to-vehicle Communications

  • 투고 : 2017.08.31
  • 심사 : 2018.03.26
  • 발행 : 2018.04.30
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초록

본 논문에서는 대표적인 6대 협력형 차량 안전 시스템 대한 잠재적인 사회적 안전효과를 분석하는 방법론을 제시하고자 한다. 협력형 차량 안전시스템의 도입 및 확산을 위해서는 사회적으로 어떠한 파급효과를 가져올지에 대하여 안전 시스템 별 정량적 분석이 필요하다. 국내에서 사고유형 기반으로 분석이 이루어진 사례들이 있으나, 사고 시나리오를 기반으로 한 분석방법론을 찾아보기 어렵다. 이러한 방법론을 제시하기 위해서 우선 승용차나 승합차만을 대상으로 한 국내 사망교통사고 데이터를 기반으로 사고 시나리오를 분류한다. 다음으로 사고 시나리오를 분석하여 협력형 차량 안전 시스템에 적용 가능한 시나리오를 선정하고 결과적으로 사망 사고 빈도수를 통해 안전 시스템의 잠재적인 사회적 안전효과를 정량적으로 제시한다. 마지막으로 안전효과의 정량적 분석을 이용하여 협력형 차량 안전 시스템의 인증을 위한 대표 평가 시나리오를 제시한다.

In this paper, a methodology to analyze the potential safe benefit of six cooperative driver assistance systems via V2V (vehicle-to-vehicle) communications is proposed. Although it is quite necessary to assess social impact with respect to new safety technologies for cooperative vehicles with V2V communications, there are few studies in Korea to predict the quantitative safety benefit analysis. In this study, traffic accident scenarios are classified based on traffic fatality between passenger cars. The sequential collision type is classified for a multiple pile-up with respect to collision direction such as forward, side, head-on collisions. Then movement of surrounding vehicle is considered for the scenario classification. Next, the cooperative driver assistance systems such as forward collision warning, blind spot detection, and intersection movement assistance are related with the corresponding accident scenarios. Finally, it is summarized how much traffic fatality may be reduced potentially due to the V2V communication based safety services.

키워드

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피인용 문헌

  1. Critical Scenario Generation for Collision Avoidance of Automated Vehicles Based on Traffic Accident Analysis and Machine Learning vol.28, pp.11, 2020, https://doi.org/10.7467/ksae.2020.28.11.817