Light-based solutions for the acceptance of facing rearward in autonomous vehicles

The introduction of autonomous vehicles into road traffic is accompanied by the development of innovative seating layouts. Concepts of such layouts often include rotatable front seats, which are supposed to enable a new level of social interaction during autonomous driving and find much approval among potential users. This contrasts with a seemingly very low willingness to be driven autonomously while sitting in the opposite direction of travel. Two reasons for this emerge, lack of trust in the autonomous vehicles and fear of motion sickness. With both being a point of concern in AVs in general, research suggests them being even more eminent when facing against the direction of travel. Based on current literature, a new model is proposed taking seating orientation and motion sickness into account. Building on this model, the use of light-based HMIs to increase transparency with respect to perception and intention of the AV is discussed. The goal of the work is to gain a more detailed understanding of the acceptance of rearward seating orientations in autonomous vehicles, incorporating trust and motion sickness

XAIR: A Framework of Explainable AI in Augmented Reality

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.Comment: Proceedings of the 2023 CHI Conference on Human Factors in Computing System