Effects of explanation types and perceived risk on trust in autonomous vehicles

Despite technological advances, trust still remains as a major issue facing autonomous vehicles. Existing studies have reported that explanations of the status of automation systems can be an effective strategy to increase trust, but these effects can differ depending on the forms of explanations and autonomous driving situations. To address this issue, this study examines the effects of explanation types and perceived risk on trust in autonomous vehicles. Three types of explanations (i.e., no, simple, and attributional explanations) are designed based on attribution theory. Additionally, four autonomous driving situations with different levels of risk are designed based on a simulator program. Results show that explanation type significantly affects trust in autonomous vehicles, and the perceived risk of driving situations significantly moderates the effect of the explanation type. At a high level of perceived risk, attributional explanations and no explanations lead to the lowest and highest values in trust, respectively. However, at a low level of perceived risk, these effects reverse.