Citation

Xu C, Ding Z, Wang C, Li Z. J. Saf. Res. 2019; 71: 41-47.

Affiliation

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.

Copyright

(Copyright © 2019, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2019.09.001

PMID

31862043

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.

Copyright © 2019 National Safety Council and Elsevier Ltd. All rights reserved.

Language: en

Keywords

Collision type; Connected and autonomous vehicle; Crash; Crash severity