Who is to blame for crashes involving autonomous vehicles? Exploring blame attribution across the road transport system

Abstract

The introduction of fully autonomous vehicles is approaching. This warrants a re-consideration of road crash liability, given drivers will have diminished control. This study, underpinned by attribution theory, investigated blame attribution to different road transport system actors following crashes involving manually driven, semi-autonomous and fully autonomous vehicles. It also examined whether outcome severity alters blame ratings. 396 participants attributed blame to five actors (vehicle driver/user, pedestrian, vehicle, manufacturer, government) in vehicle–pedestrian crash scenarios. Different and unique patterns of blame were found across actors, according to the three vehicle types. In crashes involving fully autonomous vehicles, vehicle users received low blame, while vehicle manufacturers and government were highly blamed. There was no difference in the level of blame attributed between high and low severity crashes regarding vehicle type. However, the government received more blame in high severity crashes. The findings have implications for policy and legislation surrounding crash liability.

Practitioner summary: Public views relating to blame and liability in transport accidents is a vital consideration for the introduction of new technologies such as autonomous vehicles. This study demonstrates how a systems ergonomics framework can assist to identify the implications of changing public opinion on blame for future road transport systems.

Abbreviation: ANOVA: analysis of variance; DAT: defensive attribution theory; IV: independent variable

Keywords:

• Road crashes
• autonomous vehicles
• blame attribution
• liability
• self-driving cars

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Gemma Read’s contribution to this research was funded through her Australian Research Council (ARC) Discovery Early Career Research Award [DE180101449]. Jason Thompson’s contribution was funded through his ARC Discovery Early Career Research Award [DE180101411]. Paul Salmon’s contribution was funded through his ARC Future Fellowship [FT140100681].