Attribution of blame of crash causation across varying levels of vehicle automation
Bennett, J., Challinor, K.L., Modesto Ramirez, O. and Prabhakharan, Prasannah. (2020). Attribution of blame of crash causation across varying levels of vehicle automation. Safety Science.
|Authors||Bennett, J., Challinor, K.L., Modesto Ramirez, O. and Prabhakharan, Prasannah|
Whilst public opinion towards automated vehicles is largely favourable, there are recurrent concerns around responsibility in the event of a crash (Kyriakidis et al., 2015). There is debate about the complexities regarding the legal responsibility of crashes involving automated vehicles, with opinions ranging from the driver is always accountable to the impossibility of holding an ‘automated driver’ responsible. Whilst “who is responsible” for a crash at different levels of vehicle automation has been debated, little is known about public opinion around the attribution of blame in automated vehicle crashes. In order to better understand how these might impact trust and adoption of these technologies, the present study aimed to understand public perceptions of responsibility for crashes involving different levels of automation, and the perceived consequences of that responsibility. A total of 129 undergraduate students, aged between 19 and 61 (M = 24.6, SD = 7.64) read four vignettes which detailed a pedestrian crash scenario with level of vehicle automation being manipulated in each vignette (manual driving, partially automated, highly automated and fully automated driving). Participants were asked three open-ended questions; ‘Where do you assign blame?’, ‘Based on where you assign blame, what course of action would you take from here?’, and ‘How could this event be prevented in the future?’. Results revealed that participants attributed blame to six stakeholder categories (driver, pedestrian, car, government, manufacturer and programmer). As automation increased, the proportion of participants who blamed the driver decreased, whilst those blaming the manufacturer increased. Participants commonly identified legal action against the driver, the manufacturer or both as their course of action. The proportions varied across level of automation, with legal action against the ‘driver’ still identified when the vehicle was fully automated. Furthermore, as level of automation increased, there were increased calls for automation to be reviewed, improved or completely avoided. Overwhelmingly the findings from this study highlight that the public believe that the ultimate responsibility for a crash is in the hands of the human driver, rather than the ‘automated driver’. These findings have implications for the public trust and rates of adoption of automated vehicles. Further it highlights the need for greater governance and legal frameworks around the outcomes of crashes involving automated vehicles.
|Keywords||automated vehicles; accident; autonomous vehicles; liability; self-driving vehicles; responsibility|
|Research or scholarly||Research|
|Publication process dates|
|Deposited||19 May 2021|
|ARC Funded Research||This output has not been funded, wholly or partially, under the Australian Research Council Act 2001|
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