Abstract
Automated vehicles (AV) have the potential to decrease driving-related accidents and traffic congestion and to reduce fuel consumption and carbon emissions. However, because of a lack of trust and acceptance, their widespread adoption is far from certain. One approach researchers have taken to promote trust and acceptance of AVs is to decrease the uncertainty associated with their actions by providing explanations. AV explanations are the reasons the AV provides to make its actions easier to understand. There is now a nascent but rapidly growing body of research on AV explanations. Yet, answers to basic questions like whether or when AV explanations are effective still elude us. To better understand what has been done and what should be done with regard to AV explanations, we present a review of the literature, discuss the findings and identify several important future research directions.
Highlights
D ESPITE technological advancements, the widespread adoption of automated vehicles (AVs) is far from certain
This study examined the “Trolley problem” and found that providing no explanation was better in terms of trust than providing a what + why explanation when the AV remained in the original lane where it would crash into five persons
Much of what we think we know about AV explanations could be undermined if human emotions and behavior in driving simulators does not correspond to real road driving
Summary
D ESPITE technological advancements, the widespread adoption of automated vehicles (AVs) is far from certain. We: (1) survey the literature on explanations provided by Levels 2–5 AVs, which control and perform some or all aspects of the driving [22]; (2) present and discuss the findings with regard to the effectiveness of AV explanations on driver outcomes (e.g., trust); (3) identify remaining challenges and present future research suggestions. This paper represents a deep reflection on the existing AV explanation literature and provides an important starting point for future research on AV explanations. The paper identifies important gaps in the AV explanation literature and offers guidance for future research.
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