Citation

Wotton MEL, Bennett JM, Modesto O, Challinor KL, Prabhakharan P. Transp. Res. F Traffic Psychol. Behav. 2022; 87: 219-235.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.trf.2022.03.021

PMID

unavailable

Abstract

Vehicle crashes are one of the leading causes of human deaths worldwide, with crashes predominately attributed to failures of human drivers. Whilst increasing vehicle automation is argued to reduce road crashes via decreased driver involvement, automation also raises concerns around driver blame and stakeholder responsibility. This study examines blame for crash scenarios across four different forms of driver distraction behaviours (phone, sleep, work and driving under the influence), and across four levels of vehicle automation (no automation [manual], partially automated, highly automated, fully automated), using a mixed (qualitative and quantitative) methods approach. Participants (n = 205) were randomised into one of the four levels of vehicle automation and were presented with vignette crash scenarios involving a pedestrian being hit by a vehicle.

RESULTS revealed that scenarios varying driver behavior at the time of the crash, had no significant impact on participants' blame attribution or selected course of action. The qualitative analysis revealed that despite semantic distinction between some driver behaviours, drivers were deemed responsible for the crash. As automation increased, attribution of blame towards the driver decreased, but did not disappear. Blame simultaneously increased towards other stakeholders including the manufacturer and the government, as level of automation increased. These findings mirror that of previous research and further highlight the need for legal frameworks for crashes with automated vehicles, irrespective of driver behaviours.

Language: en