Capabilities for automated driving system (ADS)-equipped vehicles have been expanding over the past decade. Research has explored integrating augmented reality (AR) interfaces in ADS-equipped vehicles to improve drivers’ situational awareness, performance, and trust. This paper systematically reviewed AR visualizations for in-vehicle vehicle-driver communication from 2012 to 2022. The review first identified meta-data and methodological trends before aggregating findings from distinct AR interfaces and corresponding subjective and objective measures. Prominent subjective measures included acceptance, trust, and user experience; objective measures comprised various driving behavior or eye-tracking metrics. Research more often evaluated simulated AR interfaces, presented through windshields, and communicated object detection or intended maneuvers, in level 2 ADS. For object detection, key visualizations included bounding shapes, highlighting, or symbols. For intended route, mixed results were found for world-fixed verse screen-fixed arrows. Regardless of the AR design, communicating the ADS’ actions or environmental elements was beneficial to drivers, though presenting clear, relevant information was more favorable. Gaps in the literature that yet to be addressed include longitudinal effects, impaired visibility, contextual user needs, system reliability, and, most notably, inclusive design. Regardless, the review supports that integrating AR interfaces in ADS-equipped vehicles can lead to higher trust, acceptance, and safer driving performances.

Objective The current paper conducted two parallel studies to explore user experiences of well-being conversational agents (CAs) and identify important features for engagement. Background Students transitioning into university life take on greater responsibility, yet tend to sacrifice healthy behaviors to strive for academic and financial gain. Additionally, students faced an unprecedented pandemic, leading to remote courses and reduced access to healthcare services. One tool designed to improve healthcare accessibility is well-being CAs. CAs have addressed mental health support in the general population but have yet to address physical well-being support and accessibility to those in disadvantaged socio-economic backgrounds where healthcare access is further limited. Method Study One comprised a thematic analysis of mental health applications featuring CAs from the public forum, Reddit. Study Two explored emerging usability themes of an SMS-based CA designed to improve accessibility to well-being services alongside a commercially available CA, Woebot. Results Study One identified several themes, including accessibility and availability, communication style, and anthropomorphism as important features. Study Two identified themes such as user response modality, perceived CA role, question specificity, and conversation flow control as critical for user engagement. Conclusion Various themes emerged from individuals’ experiences regarding CA features, functionality, and responses. The mixed experiences relevant to the communication and conversational styles between the CA and the user suggest varied motivations for using CAs for mental and physical well-being. Application Practical recommendations to encourage continued use include providing dynamic response modalities, anthropomorphizing the chatbot, and calibrating expectations early.

The current study aimed to further analyze Schadenberg et al.’s (2021) dataset examining perceived social attributions after observing human-robot interactions. In their study, visibility of an external cause was found to significantly predict perceived competence of the Robot Social Attributes Scale (RoSAS) only. However, no demographic variable was considered in the analyzes. Therefore, gender was analyzed as a moderator between visibility and participants’ perceived competence of the robot. Additionally, a Confirmatory Factor Analysis was conducted on the RoSAS measure to examine its validity in the current context. Results indicated that gender did not significantly moderate the relationship between visibility and perceived competence. This finding could be explained by the RoSAS demonstrating poor fit with the data even though the measure indicated high internal reliability. In light of these results, the current study advocates for further psychometric validation of newer scales across varying conditions, especially within the social robotics field.

Vehicles with autonomous features are more prevalent in today’s society, though as the level of autonomation increases, so does the vehicle system’s control of the vehicle. As the driving control shifts from human to the vehicle system, concerns arise regarding the attribution of responsibility and blame following critical events (e.g., collisions or near-misses). In this work-in-progress study, we aim to understand how the public attributes blame and praise to both humans and autonomous vehicles (AVs) following critical events. In addition, we examine how an AI driving assistant that administers Monitoring Requests influences blame and praise attributions. Furthermore, we examine differences in acceptance, trust, and perceived anthropomorphism between an AV with and without an AI driving assistant. Preliminary results are provided followed by a discussion of the expected results and potential impact for research and legal issues.

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