Automated Vehicle Crash Rate Comparison Using Naturalistic Data

This study assessed driving risk for the United States nationally and for the Google Self-Driving Car project. Driving safety on public roads was examined in three ways. The total crash rates for the Self-Driving Car and the national population were compared to (1) rates reported to the police, (2) crash rates for different types of roadways, and (3) scenarios that give rise to unreported crashes. First, crash rates from the Google Self-Driving Car project per million miles driven, broken down by severity level were calculated. The Self-Driving Car rates were compared to rates developed using national databases which draw upon police-reported crashes and rates estimated from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS). Second, SHRP 2 NDS data were used to calculate crash rates for three levels of crash severity on different types of roads, broken down by the speed limit and geographic classification (termed “locality” in the study; e.g., urban road, interstate). Third, SHRP 2 NDS data were again used to describe various scenarios related to crashes with no known police report. This analysis considered whether such factors as driver distraction or impairment were involved, or whether these crashes involved rear-end collisions or road departures.

Crashes within the SHRP 2 NDS dataset were ranked according to severity for the referenced event/incident type(s) based on the magnitude of vehicle dynamics (e.g., high Delta-V or acceleration), the presumed amount of property damage (less than or greater than $1,500, airbag deployment), knowledge of human injuries (often unknown in this dataset), and the level of risk posed to the drivers and other road users (Antin, et al., 2015; Table 1). Google Self-Driving Car crashes were also analyzed using the methods developed for the SHRP 2 NDS in order to determine crash severity levels and fault (using these methods, none of the vehicles operating in autonomous mode were deemed at fault in crashes).