논문 상세보기
pp.35-44
The National Highway Traffic Safety Administration (NHTSA) and the California Department of Motor Vehicles (CA DMV) collect and utilize data from traffic accidents caused by Automated Driving Systems (ADS) driving on real roads, as a policy. Leading autonomous driving technology companies such as Tesla and Waymo collect their own driving and accident data and use them for technology advancement. ADS traffic accident data that occur when driving on real roads are valuable for identifying problems in unexpected situations. This study analyzes the risk of traffic accidents by Operational Design Domain (ODD) on ADS traffic accident data that occurred while driving on an actual road and aims to present a road traffic law-based driving ability evaluation scenario in a complex ODD configuration in high-risk situations, wherein an ADS can be particularly vulnerable in mixed traffic situations. The actual road traffic accident data of ADS from 2,289 accidents as provided by the NHTSA were analyzed. Analysis of the characteristics of ADS traffic accidents revealed that accidents occurred mainly on ordinary ODDs with high traffic demand during actual road driving, that is, on dry roads during clear days and daylight. In traffic situations including ADS and Human Driving Vehicle(HDV), approximately 40% of traffic accidents were confirmed to have occurred because of HDV colliding with stationary ADS and occurred in unexpected situations, such as changing the HDV when driving straight ahead of the ADS. Results of analyzing the risk of traffic accidents on the driving status of ADS by ODD, showed that the risk of traffic accidents that occurred while the ADS was driving straight ahead was 2.27, with dry road conditions, sunny weather, and a road speed limit of 21 to 30 mph at night when streetlights were turned on. Thus, the ADS road traffic law-based driving ability evaluation scenario can be used to evaluate whether to recognize and respond to accident risk situations by developing ADS road traffic law-based driving ability evaluation scenarios for situations vulnerable to accidents due to HDV cut-in in traffic situations that include ADS and HDV. In future, this can be used as basic data for preparing related regulations and institutional devices, such as traffic accident investigations and driving ability evaluations by ADS.
1. 서론
1.1. 연구 배경 및 목적
1.2. 연구 범위 및 방법
2. 이론적 배경 및 선행연구 검토
2.1. ADS의 ODD 국가표준
2.2. ADS 교통사고 특성 분석
2.3. 선행 연구와의 차별성
3. 자율주행시스템 교통사고 데이터
3.1. ADS 교통사고 데이터
3.2. ODD에 따른 ADS 교통사고 데이터 분류
3.3. ODD별 ADS 교통사고 특성
3.4. ADS 주행 상태별 교통사고 특성
3.5. ADS 교통사고 빈도분석 결과
4. 자율주행시스템 교통사고 위험도 분석
4.1. 교통사고 위험도 산정
4.2. ODD별 ADS 교통사고 위험도 분석 결과
4.3. ADS 교통사고 위험도 분석 결과
5. 자율주행시스템 고위험 상황 운전능력평가시나리오 개발
5.1. ADS 운전능력평가 시나리오 구성
5.2. 고위험 상황에서 운전능력평가 시나리오 개발
6. 결론
감사의 글
REFERENCES
