Training postural control in virtual reality to self-enhance motion comfort in automated vehicles

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Abstract

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.