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Cybersickness (CS) is a pressing issue in virtual reality (VR) systems. While various mitigation methods (MMs) have been developed to counteract CS, their effects on human behavior remain largely unexplored, raising concerns about their potential applications. Using Jens Rasmussen's skill--rule--knowledge (SRK) model as a framework, our study investigated the effects of two widely adopted MMs---dynamic field of view and dynamic blurring---in VR. We compared these methods to a baseline condition where no MM was applied. We designed three VR tasks that align with the behavioral levels of the SRK model. In a within-subject study (N = 22), participants completed each task using these MMs. We measured task performance, CS symptoms, and locomotion control. Additionally, qualitative feedback was collected. Our results revealed that neither MM significantly alleviated CS across different VR scenarios. Furthermore, while some participants found MMs helpful, a larger portion reported visual hindrances, and a significant performance drop was measured in the skill-based task. More critically, participants indicated behavioral adaptations in response to the MMs, including changes in locomotion strategies and viewing behavior. Potential causes and implications were discussed. In conclusion, MMs offer promise, but their application necessitates a nuanced understanding of their impacts. We recommend a context-sensitive approach when designing and integrating MMs, prioritizing both maximizing CS mitigation and minimizing interference with the natural behaviors of users.
Cybersickness (CS) is a pressing issue in virtual reality (VR) systems. While various mitigation methods (MMs) have been developed to counteract CS, their effects on human behavior remain largely unexplored, raising concerns about their potential applications. Using Jens Rasmussen's skill--rule--knowledge (SRK) model as a framework, our study investigated the effects of two widely adopted MMs---dynamic field of view and dynamic blurring---in VR. We compared these methods to a baseline condition where no MM was applied. We designed three VR tasks that align with the behavioral levels of the SRK model. In a within-subject study (N = 22), participants completed each task using these MMs. We measured task performance, CS symptoms, and locomotion control. Additionally, qualitative feedback was collected. Our results revealed that neither MM significantly alleviated CS across different VR scenarios. Furthermore, while some participants found MMs helpful, a larger portion reported visual hindrances, and a significant performance drop was measured in the skill-based task. More critically, participants indicated behavioral adaptations in response to the MMs, including changes in locomotion strategies and viewing behavior. Potential causes and implications were discussed. In conclusion, MMs offer promise, but their application necessitates a nuanced understanding of their impacts. We recommend a context-sensitive approach when designing and integrating MMs, prioritizing both maximizing CS mitigation and minimizing interference with the natural behaviors of users.
Highly integrated information sharing among people, vehicles, roads, and cloud systems, along with the rapid development of autonomous driving technologies, has spurred the evolution of automobiles from simple “transportation tools” to interconnected “intelligent systems”. The intelligent cockpit is a comprehensive application space for various new technologies in intelligent vehicles, encompassing the domains of driving control, riding comfort, and infotainment. It provides drivers and passengers with safety, comfort, and pleasant driving experiences, serving as the gateway for traditional automobile manufacturing to upgrade towards an intelligent automotive industry ecosystem. This is the optimal convergence point for the intelligence, connectivity, electrification, and sharing of automobiles. Currently, the form, functions, and interaction methods of the intelligent cockpit are gradually changing, transitioning from the traditional “human adapts to the vehicle” viewpoint to the “vehicle adapts to human”, and evolving towards a future of natural interactive services where “humans and vehicles mutually adapt”. This article reviews the definitions, intelligence levels, functional domains, and technical frameworks of intelligent automotive cockpits. Additionally, combining the core mechanisms of human–machine interactions in intelligent cockpits, this article proposes an intelligent-cockpit human–machine interaction process and summarizes the current state of key technologies in intelligent-cockpit human–machine interactions. Lastly, this article analyzes the current challenges faced in the field of intelligent cockpits and forecasts future trends in intelligent cockpit technologies.
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