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

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Derivation of Driving Stability Indicators for Autonomous Vehicles Based on Analyzing Waymo Open Dataset

Waymo Open Dataset 기반 자율차의 주행행태분석을 통한 주행안정성 평가지표 도출

  • Hoyoon Lee (Dept. of Transportation and Logistics Eng., Hanyang University ERICA) ;
  • Jeonghoon Jee (Dept. of Transportation and Logistics Eng., Hanyang University ERICA) ;
  • Cheol Oh (Dept. of Transportation and Logistics Eng., Hanyang University ERICA) ;
  • Hoseon Kim (Dept. of Smart City Eng., Hanyang University ERICA)
  • 이호윤 (한양대학교 ERICA 교통.물류공학과) ;
  • 지정훈 (한양대학교 ERICA 교통.물류공학과) ;
  • 오철 (한양대학교 ERICA 교통.물류공학과) ;
  • 김호선 (한양대학교 ERICA 스마트시티공학과)
  • Received : 2024.06.10
  • Accepted : 2024.08.09
  • Published : 2024.08.31
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Abstract

As autonomous vehicles are allowed to drive on public roads, there is an increasing amount of on-road data available for research. It has therefore become possible to analyze impacts of autonomous vehicles on traffic safety using real-world data. It is necessary to use indicators that are well-representative of the driving behavior of autonomous vehicles to understand the implications of them on traffic safety. This study aims to derive indicators that effectively reflect the driving stability of autonomous vehicles by analyzing the driving behavior using the Waymo Open Dataset. Principal component analysis was adopted to derive indicators with high explanatory capability for the dataset. Driving stability indicators were separated into longitudinal and lateral ones. The road segments on the dataset were divided into four based on the characteristics of each, which were signalized and unsignalized intersections, tangent road section, and curved road section. The longitudinal driving stability was 35.48% higher in the curved road sections compared to the unsignalized intersections. With regard to the lateral driving stability, the driving stability was 76.08% higher in the signalized intersections than in the unsignalized intersections. The comparison between curved and tangent road segments showed that tangent roads are 146.87% higher regarding lateral driving stability. The results of this study are valuable for the further research to analyze the impact of autonomous vehicles on traffic safety using real-world data.

무인 자율차의 공도 주행이 허용됨에 따라 연구에 활용가능한 자율차의 실도로 주행 데이터가 증가하는 추세이다. 따라서 혼합교통류 상황에서 실제 자율차가 교통안전에 미치는 영향을 분석할 수 있게 되었다. 자율차가 교통안전에 미치는 영향을 파악하기 위해서는 자율차의 주행행태를 효과적으로 반영할 수 있는 평가지표의 활용이 요구된다. 본 연구의 목적은 Waymo Open Dataset을 통해 자율차의 주행행태를 분석하여 단속류 도로 구간별 주행안정성을 평가하기 위한 주요 지표를 도출하는 것이다. 주성분 분석을 통해 단속류 도로 구간별 데이터에 대한 설명력이 높은 평가지표를 선별하고 주요 평가지표로 정의하였다. 이때, 종방향과 횡방향 주행 안정성을 구분하여 각각에 대한 주요 평가지표를 제시하였다. 이후 동일한 주요 평가지표가 도출된 단속류 도로 구간을 대상으로 주행안정성을 비교하였다. 비신호교차로 대비 곡선 단일로 구간에서 종방향 주행안정성이 약 35.48% 높게 도출되었다. 횡방향 주행안정성의 경우 비신호교차로 대비 신호교차로 구간에서 주행안정성이 76.08% 높게 도출되었으며, 직선 단일로가 곡선 단일로에 비해 146.87% 높은 것으로 도출되었다. 본 연구의 결과는 자율차의 실도로 주행 데이터를 활용한 자율차의 교통안전 영향 분석 시 기초 자료로 활용할 수 있을 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었습니다(RS-2022-00143579, 자율주행 Lv.4/4+ 공유차(Car-Sharing) 서비스 기술 개발).

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