Cybersickness is a major obstacle to immersive VR experiences, and various strategies have been proposed to overcome it. In particular, providing artificial sensory stimuli is considered a prominent approach, which improves presence and real-virtual congruence, thereby helping the situation. In this study, we evaluated the effectiveness of artificial sensory stimuli in inducing virtual walking sensations in mitigating cybersickness. We conducted two experiments involving a total of 80 participants: (1) a static user experiment ($\mathrm{N}=40$), in which participants used controller-based steering without physical movement, and (2) a dynamic user experiment ($\mathrm{N}=40$), in which participants walked on an omnidirectional treadmill, enabling virtual movement similar to real walking. In both experiments, participants were divided into two groups based on the presence or absence of vibrotactile feedback. We collected heart rate and subjective discomfort data during exposure to a VR environment and then evaluated cybersickness and presence. Across both experiments, the results indicated that virtual walking sensations induced through vibrotactile feedback significantly reduced cybersickness without negatively affecting the perceived task load or presence. Additionally, in the dynamic user locomotion experiment, a greater reduction in cybersickness was observed when vibrotactile feedback was present. These findings suggest that even if the real-virtual sensations are synchronized, vibrotactile feedback may induce cognitive distraction that further mitigates cybersickness.

In the rapid development of artificial intelligence(AI), explainability(XAI) in particular plays a critical role in ensuring the reliability and safety of AI systems. Existing XAI studies mainly focus on interpreting the process by which a model derives a specific conclusion, which is essential for providing transparency in AI decision-making in high-risk applications. However, most studies focus on how AI makes correct predictions, and there is a relative lack of research on how to analyze and effectively correct the root causes of errors that occur during the data collection process. Errors that occur during the data collection process can lead to biased model learning, misclassification, and decreased overall system reliability. In general, AI pipelines regard such errors as simple noise and respond by applying outlier detection or noise removal techniques to refine the data, but these approaches have limitations in resolving the root causes of errors. In this study, we propose a feedback-based error correction framework that goes beyond simply detecting data errors to explain why the errors occurred and quickly correct them through a more systematic approach. The proposed framework detects errors early in the data collection phase, classifies the types of errors, identifies the error occurrence mechanism through question-answering($\mathbf{Q A}$)-based cause analysis, and constructs a feedback loop to correct errors through automated correction or human intervention based on the results, thereby continuously improving data quality. In particular, it focuses on data error management in high-risk environments such as autonomous driving, and proposes an environment-adaptive data error detection and correction technique.

As interactions between Big Data and AI increasingly raise concerns about bias and ethical errors in decision-making, establishing trust within the big data ecosystem has become essential. As data types diversify and the volume of information grows exponentially, environments where real-time information processing is crucial, such as autonomous driving and IoT, require fair and reliable system management. This study introduces a trust-centric big data ecosystem designed to address challenges through an ethical framework for autonomous driving. The core of the big data ecosystem is a trust management layer that ensures data integrity and transparency, and integrated with advanced algorithms for real-time bias detection and mitigation. The proposed framework incorporates a cultural context module that enables ethically sound and culturally appropriate decision-making, along with bias detection, self-inspection, and risk assessment. By embedding trust and ethics into every layer of data processing and decision-making, this system ensures fairness and transparency in big data processing, thus contributing to the advancement of the big data ecosystem.