Automated vehicles (AVs) are on the edge of being available on the mass market. Research often focuses on technical aspects of automation, such as computer vision, sensing, or artificial intelligence. Nevertheless, researchers also identified several challenges from a human perspective that need to be considered for a successful introduction of these technologies. In this paper, we first analyze human needs and system acceptance in the context of AVs. Then, based on a literature review, we provide a summary of current research on in-car driver-vehicle interaction and related human factor issues. This work helps researchers, designers, and practitioners to get an overview of the current state of the art.

Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.

Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) empower users to experience digital realities. Known as distinct technology classes, the lines between them are becoming increasingly blurry with recent technological advancements. New systems enable users to interact across technology classes or transition between them—referred to as cross-reality systems . Nevertheless, these systems are not well understood. Hence, in this article, we conducted a scoping literature review to classify and analyze cross-reality systems proposed in previous work. First, we define these systems by distinguishing three different types. Thereafter, we compile a literature corpus of 306 relevant publications, analyze the proposed systems, and present a comprehensive classification, including research topics, involved environments, and transition types. Based on the gathered literature, we extract nine guiding principles that can inform the development of cross-reality systems. We conclude with research challenges and opportunities.

This paper analyses Human-Computer Interaction (HCI) literature reviews to provide a clear conceptual basis for authors, reviewers, and readers. HCI is multidisciplinary and various types of literature reviews exist, from systematic to critical reviews in the style of essays. Yet, there is insufficient consensus of what to expect of literature reviews in HCI. Thus, a shared understanding of literature reviews and clear terminology is needed to plan, evaluate, and use literature reviews, and to further improve review methodology. We analysed 189 literature reviews published at all SIGCHI conferences and ACM Transactions on Computer-Human Interaction (TOCHI) up until August 2022. We report on the main dimensions of variation: (i) contribution types and topics; and (ii) structure and methodologies applied. We identify gaps and trends to inform future meta work in HCI and provide a starting point on how to move towards a more comprehensive terminology system of literature reviews in HCI.

Nowadays, Augmented and Virtual Reality devices are widely available and are often shared among users due to their high cost. Thus, distinguishing users to offer personalized experiences is essential. However, currently used explicit user authentication (e.g., entering a password) is tedious and vulnerable to attack. Therefore, this work investigates the feasibility of implicitly identifying users by their hand tracking data. In particular, we identify users by their uni- and bimanual finger behavior gathered from their interaction with eight different universal interface elements, such as buttons and sliders. In two sessions, we recorded the tracking data of 16 participants while they interacted with various interface elements in Augmented and Virtual Reality. We found that user identification is possible with up to 95% accuracy across sessions using an explainable machine learning approach. We conclude our work by discussing differences between interface elements, and feature importance to provide implications for behavioral biometric systems. A video abstract of this work is available online at: https://identifying-users-by-hand-tracking-data.hcigroup.de

Behavioral biometrics has recently become a viable alternative method for user identification in Virtual Reality (VR). Its ability to identify users based solely on their implicit interaction allows for high usability and removes the burden commonly associated with security mechanisms. However, little is known about the temporal stability of behavior (i.e., how behavior changes over time), as most previous works were evaluated in highly controlled lab environments over short periods. In this work, we present findings obtained from a remote field study (N = 15) that elicited data over a period of eight weeks from a popular VR game. We found that there are changes in people’s behavior over time, but that two-session identification still is possible with a mean F1-score of up to 71%, while an initial training yields 86%. However, we also see that performance can drop by up to over 50 percentage points when testing with later sessions, compared to the first session, particularly for smaller groups. Thus, our findings indicate that the use of behavioral biometrics in VR is convenient for the user and practical with regard to changing behavior and also reliable regarding behavioral variation.