Citation

Teoh ER, Kidd DG. J. Saf. Res. 2017; 63: 57-60.

Affiliation

Insurance Institute for Highway Safety, United States.

Copyright

(Copyright © 2017, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2017.08.008

PMID

29203024

Abstract

INTRODUCTION: Automated driving represents both challenges and opportunities in highway safety. Google has been developing self-driving cars and testing them under employee supervision on public roads since 2009. These vehicles have been involved in several crashes, and it is of interest how this testing program compares to human drivers in terms of safety.

METHODS: Google car crashes were coded by type and severity based on narratives released by Google. Crash rates per million vehicle miles traveled (VMT) were computed for crashes deemed severe enough to be reportable to police. These were compared with police-reported crash rates for human drivers. Crash types also were compared.

RESULTS: Google cars had a much lower rate of police-reportable crashes per million VMT than human drivers in Mountain View, Calif., during 2009-2015 (2.19 vs 6.06), but the difference was not statistically significant. The most common type of collision involving Google cars was when they got rear-ended by another (human-driven) vehicle. Google cars shared responsibility for only one crash.

CONCLUSIONS: These results suggest Google self-driving cars, while a test program, are safer than conventional human-driven passenger vehicles; however, currently there is insufficient information to fully examine the extent to which disengagements affected these results. PRACTICAL APPLICATION: Results suggest that highly-automated vehicles can perform more safely than human drivers in certain conditions, but will continue to be involved in crashes with conventionally-driven vehicles.

Copyright © 2017. Published by Elsevier Ltd.

Language: en

Keywords

Autonomous vehicle; Driving automation; Highway safety; Motor vehicle crashes; Self-driving