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

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Analysis of Autonomous Vehicles Risk Cases for Developing Level 4+ Autonomous Driving Test Scenarios: Focusing on Perceptual Blind

Lv 4+ 자율주행 테스트 시나리오 개발을 위한 자율주행차량 위험 사례 분석: 인지 음영을 중심으로

  • Seung min Oh (Cho Chun Shik Graduate School of Mobility, KAIST) ;
  • Jae hee Choi (Cho Chun Shik Graduate School of Mobility, KAIST) ;
  • Ki tae Jang (Cho Chun Shik Graduate School of Mobility, KAIST) ;
  • Jin won Yoon (Mechanical Engineering Research Institute, KAIST)
  • 오승민 (한국과학기술원 조천식모빌리티대학원) ;
  • 최재희 (한국과학기술원 조천식모빌리티대학원) ;
  • 장기태 (한국과학기술원 조천식모빌리티대학원) ;
  • 윤진원 (한국과학기술원 기계기술연구소 )
  • Received : 2024.03.25
  • Accepted : 2024.04.18
  • Published : 2024.04.30
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Abstract

With the advancement of autonomous vehicle (AV) technology, autonomous driving on real roads has become feasible. However, there are challenges in achieving complete autonomy due to perceptual blind areas, which occur when the AV's sensory range or capabilities are limited or impaired by surrounding objects or environmental factors. This study aims to analyze AV accident patterns and safety issues of perceptual blind area that may occur in urban areas, with the goal of developing test scenarios for Level 4+ autonomous driving. It utilized AV accident data from the California Department of Motor Vehicles (DMV) to compare accident patterns and characteristics between AVs and conventional vehicles based on activation status of autonomous mode. It also categorized AV disengagement data to identify types and real-world cases of disengagements caused by perceptual blind areas. The analysis revealed that AVs exhibit different accident types due to their safe driving maneuvers, and three types of perceptual blind area scenarios were identified. The findings of this study serve as crucial foundational data for developing Level 4+ autonomous driving test scenarios, enabling the design of efficient strategies to mitigate perceptual blind areas in various scenarios. This, in turn, is expected to contribute to the effective evaluation and enhancement of AV driving safety on real roads.

자율주행차량(AV)의 기술 발전으로 실도로 내 자율주행이 가능해졌지만, 주변 요소로 AV의 인지 범위 또는 능력이 제한되는 인지 음영으로 완전한 자율주행에 어려움이 있다. 오늘날 Lv 4+ 자율주행 테스트 시나리오를 개발하기 위해서는 실제 도로에서 발생할 수 있는 다양한 인지 음영 상황을 파악하고 대비 전략을 구상하는 것이 중요하다. 따라서, 본 연구는 미국 캘리포니아 차량관리국(DMV)의 AV 사고 데이터를 통해 자율주행 모드 활성화 여부에 따라 AV와 일반차량의 사고 형태와 특성을 비교하고, AV 제어권 전환 데이터를 단계적으로 분류하여 인지 음영으로 인한 제어권 전환의 유형과 실제 사례를 도출하였다. 분석 결과, AV의 안전 운전 기동으로 일반 차량과 다른 사고 유형이 나타났으며, 3가지 유형의 인지 음영 사례를 파악하였다. 본 연구 결과는 Lv 4+ 자율주행 테스트 시나리오 개발의 중요한 기초자료가 될 것이며, 다양한 인지 음영이 고려된 테스트 시나리오를 통해 상황별 인지 음영을 해소하는 효율적인 전략을 마련할 수 있다. 이를 통해 실제 도로에서의 AV 주행 안전성을 효과적으로 평가하고 향상할 수 있을 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 2023년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행되었습니다(No.2023-00244929, 레벨4 자율주행 차량의 커넥티드 기반 인지 증강화 및 협력 자율주행 기술 개발).

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