  In order to serve better VR experiences to users, existing predictive methods
of Redirected Walking (RDW) exploit future information to reduce the number of
reset occurrences. However, such methods often impose a precondition during
deployment, either in the virtual environment's layout or the user's walking
direction, which constrains its universal applications. To tackle this
challenge, we propose a novel mechanism F-RDW that is twofold: (1) forecasts
the future information of a user in the virtual space without any assumptions,
and (2) fuse this information while maneuvering existing RDW methods. The
backbone of the first step is an LSTM-based model that ingests the user's
spatial and eye-tracking data to predict the user's future position in the
virtual space, and the following step feeds those predicted values into
existing RDW methods (such as MPCRed, S2C, TAPF, and ARC) while respecting
their internal mechanism in applicable ways.The results of our simulation test
and user study demonstrate the significance of future information when using
RDW in small physical spaces or complex environments. We prove that the
proposed mechanism significantly reduces the number of resets and increases the
traveled distance between resets, hence augmenting the redirection performance
of all RDW methods explored in this work.
