01698107

Component

This study aimed to investigate the characteristics and patterns of the connected and autonomous vehicle (CAV) involved crashes. The crash data were collected from the reports of CAV involved crash submitted to the California Department of Motor Vehicles between 2015 and 2018. The descriptive statistics analysis was employed to investigate the characteristics of CAV involved crashes in terms of crash location, weather conditions, driving mode and vehicle movement before crash occurrence, vehicle speed, collision type, crash severity and damage locations of involved vehicles. The bootstrap based binary logistic regressions were then developed to investigate the factor contributing to the collision type and severity of CAV involved crashes. 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.

This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.

19-01662

English

5p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References (12) ; Tables

Autonomous vehicles; Connected vehicles; Crash causes; Crash characteristics; Crash severity; Mathematical models

California

Highways; Safety and Human Factors; Vehicles and Equipment

Transportation Research Board Annual Meeting 2019 Paper #19-01662

TRIS, TRB, ATRI

Dec 7 2018 9:46AM