Don't fail me! The Level 5 Autonomous Driving Information Dilemma regarding Transparency and User Experience

Abstract

Autonomous vehicles can behave unexpectedly, as automated systems that rely on data-driven machine learning have shown to infer false predictions or misclassifications, e.g., due to stickers on traffic signs, and thus fail in some situations. In critical situations, system designs must guarantee safety and reliability. However, in non-critical situations, the possibility of failures resulting in unexpected behaviour should be considered, as they negatively impact the passenger's user experience and acceptance. We analyse if an interactive conversational user interface can mitigate negative experiences when interacting with imperfect artificial intelligence systems. In our quantitative interactive online survey (N=113) and comparative qualitative Wizard of Oz study (N=8), users were able to interact with an autonomous SAE level 5 driving simulation. Our findings demonstrate that increased transparency improves user experience and acceptance. Furthermore, we show that additional information in failure scenarios can lead to an information dilemma and should be implemented carefully.