Ensuring safety remains one of the biggest challenges for the widespread adoption of automated vehicles (AVs). Remote operation of AVs is a promising approach to address this, allowing remote operators to intervene when AVs encounter edge cases. However, remote operators are out-of-the-loop from the conventional driver in vehicle-environment interaction, impacting their situation awareness and ability to safely control or assist the vehicle. In the scenario of remote driving, this is more evident since multimodal feedback is required to replicate the conventional driver-vehicle-environment-interaction. In addition to visual and auditory modalities, motion feedback has been proposed as a way to bridge the gap between remote driving and in-vehicle driving. However, since motion feedback is cost-intensive, it might hinder rapid upscaling of remote driving systems. Thus, this study evaluated whether motion feedback adds value to driving performance and experience of the remote operator in low-velocity scenarios. Driving performance and experience were assessed and compared using objective and subjective metrics in three conditions (in-vehicle driving, and remote driving with and without motion feedback). The findings show that in remote driving, motion feedback fails to provide significant improvements. When compared to in-vehicle driving, remote driving performance and experience remain significantly worse. This suggests that motion feedback, in its current form, is redundant in low-velocity scenarios and that a simplified Remote Driving Station (RDS) may be sufficient in these scenarios. Future work should optimize simplified RDS designs, enhance feedback and human-machine interfaces and explore different driving scenarios for safe and efficient remote driving.

This study investigates how deviations in avatar motion influence user motion in virtual reality (VR), specifically focusing on upper body and trunk motion in a virtual environment (VE). Previous research showed that user motion can be altered via the avatar follower effect, in which avatar deviations are followed by users. This is the first work exploring this effect for a deviation including the head. The primary objective was to explore whether these deviations could subconsciously guide user motions, potentially contributing to real-time motion sickness reduction in automated vehicles.
The experiment involved participants performing seated lateral leaning tasks where they were instructed to touch virtual goals with their heads. During some trials, the avatar unexpectedly deviated from the user's intended motion.
The results revealed that contrary to expectations, the avatar follower effect did not occur. Instead, an opposing effect was observed where participants' motions contradicted the avatar's deviations, particularly when the avatar stopped prior to the instructed goal. This effect was not influenced by the user's perspective (first or third person) or the scoring mechanism used in the game. However, individual personality traits, such as a tendency for autonomy or a focus on rewards, did affect the strength of the opposing effect.
These findings suggest that using avatar deviations to guide upper body and head motion in VR may not be effective, thus unsuited for applications such as motion sickness prevention in automated vehicles.

The aim of this thesis is to enhance motion comfort for passengers in automated vehicles (AVs) by training their postural control using virtual reality (VR). A serious game called Motion Anticipation Training Environment for Automated Vehicles (MATE-AV) is developed in Unity to train participants to adjust their posture in response to motion cues in VR. The results indicate that VR can be an effective tool for postural training, demonstrating correct alignment with motion cues during training. However, the transferability of the skills with the removal of the training cues was not statistically significant, but there are patterns that indicate the potential of this approach. This research contributes to understand how VR can be used for training in automated driving contexts, laying the foundation for future studies on long-term effectiveness and real-world applications.

Moving through immersive virtual reality (VR) is commonly achieved by physically walking in the real room or using other techniques like an omnidirectional treadmill or walk-in-place. Roomscale walking is most similar to normal walking but is limited by physical space. However, other techniques can cause user experience issues such as VR sickness, balance problems, and feeling unnatural. Newer locomotion techniques are available such as powered VR shoes, which are shoes with motorized treadmills underneath. While walking, the shoes drive the user backward and actively negate the forward velocity, reducing the needed physical space. Yet, there is little evidence of the effect of powered VR shoes on user experience, which part of this work addresses. Additionally, previous research shows mismatched VR motion (optical flow) can increase VR sickness, cognitive load, and break presence. However, full-gait locomotion studies often focus on the device, neglecting optical flow, and what is the best body part to control optical flow direction is still an open question. Therefore, we first developed a novel algorithm to convert leg-based walking to optical flow while walking on VR shoes, which may also be used for other full-gait locomotion techniques. We conducted a study with 20 participants to find which of four optical flow implementations, differing in VR motion direction, resulted in the best user experience. These direction conditions were based on body-mounted trackers: i) head orientation, ii) hip orientation, iii) standing foot velocity direction, and iv) average orientation of both feet. Head-oriented walking resulted in a significantly worse user experience compared to other conditions, with no significant differences among any other conditions. Additionally, we found no effect of optical flow on VR sickness, Mental Effort, and Presence, contrary to previous studies, but instead significant differences in Ease of Use, Input responsiveness, and Appropriateness, and indication that other user experience factors might be impacted more. Finally, we discovered that walking on VR shoes, although not completely comfortable and natural, was learnable within 10 minutes for all participants under 60 years old.