Machine Learning Application for Real-Time Simulator
Communication avec acte
Author
HADADI, Azadeh
469668 Institut für Informationsmanagement im Ingenieurwesen [IMI]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
469668 Institut für Informationsmanagement im Ingenieurwesen [IMI]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
CHARDONNET, Jean-Rémy
301320 École Nationale Supérieure d'Arts et Métiers [ENSAM]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
1001263 Université Polytechnique Hauts-de-France [UPHF]
226175 Joint Robotics Laboratory [CNRS-AIST JRL ]
301320 École Nationale Supérieure d'Arts et Métiers [ENSAM]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
1001263 Université Polytechnique Hauts-de-France [UPHF]
226175 Joint Robotics Laboratory [CNRS-AIST JRL ]
Abstract
This paper presents a groundbreaking research initiative that focuses on the development of an intelligent architecture for Adaptive Virtual Reality Systems (AVRS) in immersive virtual environments. The primary objective of this architecture is to enable real-time artificial intelligence training and adapt the virtual environment based on user states or external parameters. In a case study focused on detecting cybersickness, an undesired side effect in immersive virtual environments, we utilized this architecture to train an artificial intelligence model and personalize it for individual users in a driving simulator application. By leveraging the capabilities of this architecture, we can optimize virtual reality experiences for individual users, leading to increased comfort. We evaluated the system’s performance in terms of memory usage, CPU and GPU usage, temperature monitoring, frame rate, and network performance, and our results demonstrated the efficiency of our proposed architecture.
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