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Current virtual reality systems are typically limited by performance/cost,usability (size), or a combination of both. By using a networked client/serverenvironment, we have solved these limitations for the client. However, in doingso we have introduced a new problem, namely increased latency. Interactivenetworked virtual environments such as games and simulations have existed fornearly as long as the Internet and have consistently faced latency issues. Wepropose a solution for negating the effects of latency for interactivenetworked virtual environments with lightweight clients, with respect to theserver being used. The proposed method extrapolates future client states to beincorporated in the server's updates, which helps to synchronize actions on theclient-side and the results coming from the server. We refer to this approachas predictive simulation. In addition to describing our method, in this paper,we look at extrapolation methods because the success of our predictivesimulation method is dependent on strong predictions. We focus on regressionmethods and briefly examine the use of artificial neural networks.

Predictive Simulation: Using Regression and Artificial Neural Networks to Negate Latency in Networked Interactive Virtual Reality