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Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data.
Huang, Helai; Huang, Xiangzhi; Zhou, Rui; Zhou, Hanchu; Lee, Jaeyoung Jay; Cen, Xuekai.
Afiliación
  • Huang H; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China.
  • Huang X; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China; School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China.
  • Zhou R; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China. Electronic address: arayzhou@csu.edu.cn.
  • Zhou H; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China.
  • Lee JJ; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China; Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, USA; School of Civil and Environmental Engineering, Faculty of Engineering, Queen
  • Cen X; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China.
Accid Anal Prev ; 203: 107616, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38723335
ABSTRACT
Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios. To achieve this objective, a clustering method was used to construct typical testing scenarios based on the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. Initially, 222 car-to-powered two-wheelers (PTWs) crashes and 180 car-to-car crashes were reconstructed from CIMSS-TA database. Second, six variables were extracted and analyzed, including the motion of the two vehicles involved, relative movement, lighting condition, road condition, and visual obstruction. Third, these variables were clustered using the k-medoids algorithm, identifying five typical pre-crash scenarios for car-to-PTWs and seven for car-to-car. Additionally, we extracted the velocities and surrounding environmental information of the crash-involved parties to enrich the scenario description. The approach used in this study used in-depth case review and thus provided more insightful information for identifying and quantifying representative high-risk scenarios than prior studies that analyzed overall descriptive variables from Chinese crash databases. Furthermore, it is crucial to separately test car-to-car scenarios and car-to-PTWs scenarios due to their distinct motion characteristics, which significantly affect the resulting typical scenarios.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Seguridad / Automóviles / Accidentes de Tránsito Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Accid Anal Prev Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Seguridad / Automóviles / Accidentes de Tránsito Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Accid Anal Prev Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido