Chengcheng Xu1, Zijian Ding1, Chen Wang2, Zhibin Li1. 1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing 210096, China; School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China. 2. Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing 210096, China; School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China. Electronic address: wkobec@hotmail.com.
Abstract
INTRODUCTION: This study aimed to investigate the characteristics and patterns of the connected and autonomous vehicle (CAV) involved crashes. METHOD: The crash data were collected from the reports of CAV involved crash submitted to the California Department of Motor Vehicles. The descriptive statistics analysis was employed to investigate the characteristics of CAV involved crashes in terms of crash location, weather conditions, driving mode, vehicle movement before crash occurrence, vehicle speed, collision type, crash severity, and vehicle damage locations. The bootstrap based binary logistic regressions were then developed to investigate the factors contributing to the collision type and severity of CAV involved crashes. RESULTS: The results suggested that the CAV driving mode, collision location, roadside parking, rear-end collision, and one-way road are the main factors contributing to the severity level of CAV involved crashes. The CAV driving mode, CAV stopped or not, CAV turning or not, normal vehicle turning or not, and normal vehicle overtaking or not are the factors affecting the collision type of CAV involved crashes.
INTRODUCTION: This study aimed to investigate the characteristics and patterns of the connected and autonomous vehicle (CAV) involved crashes. METHOD: The crash data were collected from the reports of CAV involved crash submitted to the California Department of Motor Vehicles. The descriptive statistics analysis was employed to investigate the characteristics of CAV involved crashes in terms of crash location, weather conditions, driving mode, vehicle movement before crash occurrence, vehicle speed, collision type, crash severity, and vehicle damage locations. The bootstrap based binary logistic regressions were then developed to investigate the factors contributing to the collision type and severity of CAV involved crashes. RESULTS: The results suggested that the CAV driving mode, collision location, roadside parking, rear-end collision, and one-way road are the main factors contributing to the severity level of CAV involved crashes. The CAV driving mode, CAV stopped or not, CAV turning or not, normal vehicle turning or not, and normal vehicle overtaking or not are the factors affecting the collision type of CAV involved crashes.
Authors: Juan Pablo Montero-Salgado; Jose Muñoz-Sanz; Blanca Arenas-Ramírez; Cristina Alén-Cordero Journal: Int J Environ Res Public Health Date: 2022-06-24 Impact factor: 4.614