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2024 Vol.42, Issue 2 Preview Page

Article

Derivation of Promising Driving Safety Evaluation Indicators for Mixed Traffic Situations in Traffic Simulation Environments

자율-비자율 차량 혼재 상황의 주행안전성 평가를 위한 시뮬레이션 기반 최적 평가지표 도출 연구

Minkyung KIM1
,
Hoseon KIM2
,
Young JO3
,
Cheol OH4*
,
Seolyoung LEE5
김 민경1
,
김 호선2
,
조 영3
,
오 철4*
,
이 설영5
1M.S. Student, Department of Smart City Engineering, Hanyang University, Ansan 15588, Korea
2Ph.D. Student, Department of Smart City Engineering, Hanyang University, Ansan 15588, Korea
3Senior Researcher, Department of Highway and Transportation Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi 10223, Korea
4Professor, Department of Transportation and Logistics Engineering, Hanyang University, Ansan 15588, Korea
5Deputy Research Fellow, Department of Urban Transportation Research, The Seoul Institute, Seoul 03909, Korea
1한양대학교 스마트시티공학과 석사과정
2한양대학교 스마트시티공학과 박사과정
3한국건설기술연구원 도로교통연구본부 수석연구원
4한양대학교 교통․물류공학과 교수
5서울연구원 도시교통연구실 부연구위원
*Corresponding Author
30 April 2024. pp. 152-167
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Abstract

It is necessary for deriving effective driving safety indicators, which can accurately describe the real-world traffic conditions in the simulation analysis, to evaluate mixed traffic situations including autonomous and manual vehicles. The purpose of this study is to simulate the maneuvering of autonomous vehicles through the analysis of real-world autonomous driving data and to derive promising driving safety evaluation indicators for identifying hazardous road sections in the simulation environment. A decision tree analysis was conducted to identify indicators that can closely resemble real-world hazardous road sections within the simulation. The hazardous road sections identified by driving safety analysis were utilized as the output variables for constructing a decision tree-based hazardous section classification model. The simulated evaluation indicators were used as input variables for the decision tree analysis. The average jerk was identified as the most promising indicator. The top three hazardous road sections within the Sangam Autonomous Vehicle Demonstration Zone were identified by the average jerk. Then the factors affecting driving safety for each section were discussed. The results of this study are expected to be effectively utilized for the evaluation of traffic safety in mixed traffic situations based on simulations.

Keywords
autonomous vehicle data
decision tree
driving safety indicators
mixed traffic
traffic simulation

자율-비자율 차량 혼재교통상황에서 교통안전성 평가를 위한 시뮬레이션 분석에서는 실제 교통환경을 효과적으로 설명할 수 있는 주행안전성 평가지표 도출이 요구된다. 본 연구의 목적은 Real-world 자율주행 데이터 분석을 통해 시뮬레이션 내 자율주행차 거동을 구현하고 시뮬레이션 환경에서 도로 위험구간을 식별하기 위한 최적의 주행안전성 평가지표를 도출하는 것이다. 실제 자율주행차 주행자료를 분석하여 시뮬레이션 환경에서 자율주행차의 거동을 구현하였다. 시뮬레이션 상에서 실제 도로위험구간을 유사하게 나타낼 수 있는 지표 선정을 위해 의사결정나무 분석을 수행하였다. 주행안전성 분석을 통해 도출한 도로 위험구간을 의사결정나무 기반 위험구간 분류모델 구축의 출력변수로 활용하였다. 자율주행차 거동이 구현된 시뮬레이션의 평가지표별 주행안전성 분석결과는 의사결정나무 분석의 입력변수로 활용하였다. 도로 위험구간을 가장 효과적으로 분류하는 최적 평가지표는 평균 jerk로 도출되었다. 최적의 평가지표인 평균 jerk 기반 상위 3개의 상암 자율주행차 시범운행지구 도로위험구간을 도출하였으며, 각 구간에서 주행안전성에 영향을 미치는 도로 교통요인을 제시하였다. 본 연구의 결과는 시뮬레이션 기반 혼재교통상황의 교통안전성 평가를 위해 효과적으로 사용될 것으로 기대된다.

키워드
Autonomous Vehicle Data
의사결정나무
주행안전성 평가지표
혼재교통상황
교통류 시뮬레이션
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 42
  • No :2
  • Pages :152-167
  • Received Date : 2023-12-11
  • Revised Date : 2023-12-21
  • Accepted Date : 2024-02-13
  • DOI :https://doi.org/10.7470/jkst.2024.42.2.152
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