As the use of Head-Mounted Displays in moving vehicles increases, passengers can immerse themselves in visual experiences independent of their physical environment. However, interaction methods are susceptible to physical motion, leading to input errors and reduced task performance. This work investigates the impact of G-forces, vibrations, and unpredictable maneuvers on 3D interaction methods. We conducted a field study with 24 participants in both stationary and moving vehicles to examine the effects of vehicle motion on four interaction methods: (1) Gaze&Pinch, (2) DirectTouch, (3) Handray, and (4) HeadGaze. Participants performed selections in a Fitts' Law task. Our findings reveal a significant effect of vehicle motion on interaction accuracy and duration across the tested combinations of Interaction Method x Road Type x Curve Type. We found a significant impact of movement on throughput, error rate, and perceived workload. Finally, we propose future research considerations and recommendations on interaction methods during vehicle movement.

As passengers spend more time in vehicles, the demand for non-driving related tasks (NDRTs) increases. In-car Augmented Reality (AR) has the potential to enhance passenger experiences by enabling interaction with the environment through NDRTs using world-fixed Points of Interest (POIs). However, the effectiveness of existing interaction techniques and visualization methods for in-car AR remains unclear. Based on a survey (N=110) and a pre-study (N=10), we developed an interactive in-car AR system using a video see-through head-mounted display to engage with POIs via eye-gaze and pinch. Users could explore passed and upcoming POIs using three visualization techniques: List, Timeline, and Minimap. We evaluated the system's feasibility in a field study (N=21). Our findings indicate general acceptance of the system, with the List visualization being the preferred method for exploring POIs. Additionally, the study highlights limitations of current AR hardware, particularly the impact of vehicle movement on 3D interaction.

With the transition to fully autonomous vehicles, non-driving related tasks (NDRTs) become increasingly important, allowing passengers to use their driving time more efficiently. In-car Augmented Reality (AR) gives the possibility to engage in NDRTs while also allowing passengers to engage with their surroundings, for example, by displaying world-fixed points of interest (POIs). This can lead to new discoveries, provide information about the environment, and improve locational awareness. To explore the optimal visualization of POIs using in-car AR, we conducted a field study (N = 38) examining six parameters: positioning, scaling, rotation, render distance, information density, and appearance. We also asked for intention of use, preferred seat positions and preferred automation level for the AR function in a post-study questionnaire. Our findings reveal user preferences and general acceptance of the AR functionality. Based on these results, we derived UX-guidelines for the visual appearance and behavior of location-based POIs in in-car AR.

Natural interaction between multiple users within a shared virtual environment (VE) relies on each other's awareness of the current position of the interaction partners. This, however, cannot be warranted when users employ noncontinuous locomotion techniques, such as teleportation, which may cause confusion among bystanders. In this paper, we pursue two approaches to create a pleasant experience for both the moving user and the bystanders observing that movement. First, we will introduce a Smart Avatar system that delivers continuous full-body human representations for noncontinuous locomotion in shared virtual reality (VR) spaces. Smart Avatars imitate their assigned user's real-world movements when close-by and autonomously navigate to their user when the distance between them exceeds a certain threshold, i.e., after the user teleports. As part of the Smart Avatar system, we implemented four avatar transition techniques and compared them to conventional avatar locomotion in a user study, revealing significant positive effects on the observers' spatial awareness, as well as pragmatic and hedonic quality scores. Second, we introduce the concept of Stuttered Locomotion , which can be applied to any continuous locomotion method. By converting a continuous movement into short-interval teleport steps, we provide the merits of non-continuous locomotion for the moving user while observers can easily keep track of their path. Thus, while the experience for observers is similarly positive as with continuous motion, a user study confirmed that Stuttered Locomotion can significantly reduce the occurrence of cybersickness symptoms for the moving user, making it an attractive choice for shared VEs. We will discuss the potential of Smart Avatars and Stuttered Locomotion for shared VR experiences, both when applied individually and in combination.

We introduce VR Strider, a novel locomotion user interface (LUI) for seated virtual reality (VR) experiences, which maps cycling biomechanics of the user's legs to virtual walking movements. The core idea is to translate the motion of pedaling on a mini exercise bike to a corresponding walking animation of a virtual avatar while providing audio-based tactile feedback on virtual ground contacts. We conducted an experiment to evaluate the LUI and our novel anchor-turning rotation control method regarding task performance, spatial cognition, VR sickness, sense of presence, usability and comfort in a path-integration task. The results show that VR Strider has a significant positive effect on the participants' angular and distance estimation, sense of presence and feeling of comfort compared to other established locomotion techniques, such as teleportation and joystick-based navigation. A confirmatory study further indicates the necessity of synchronized avatar animations for virtual vehicles that rely on pedalling.

We report on findings from two focus groups for designing an application for balance training at home with an augmented reality virtual coach. Following a User-Centered Design approach, we performed two focus groups with older adults at the early stages of development. Focus group participants were shown a prototype using a Meta 2 head mounted display. Their movements were tracked using a Kinect 2. The virtual coach gave balance training instructions and demonstrated their correct performance. Results suggest that, given the trade-offs of traditional health care, older adults are positive towards using an AR coach for their balance training.

We introduce Camera Time Warp (CamWarp), a novel reprojection technique for video-see-through augmented reality, which reduces the registration error between captured real-world videos and rendered virtual images. Instead of rendering the image plane locked to the virtual camera, CamWarp renders the image plane at the real-world position it was captured at, and compensates for potential artifacts. We conducted two experiments to evaluate the effectiveness of CamWarp. In the first experiment participants were asked to report subjective discomfort while moving their head in a pattern inspired by the ISO 9241-9 Fitts' Law task at different speeds while the video feed was rendered at varying frame rates. The results show that the technique can significantly reduce subjective levels of discomfort and cybersickness symptoms for all tested configurations. In the second experiment participants were asked to move physical objects on a projected path as quickly and precisely as possible. Results show a positive effect of CamWarp on speed and accuracy.