Short-term Path Prediction for Virtual Open Spaces

In predictive redirected walking applications, reliable path prediction is essential for an effective redirection. So far, most predictive redirected walking algorithms introduced many restrictions to the virtual environment in order to simplify this path prediction. Path prediction is time-consuming and is also prone to errors due to potentially impulsive and even irrational walking behaviour of humans in virtual environments. Therefore, many applications confine users in narrow virtual corridors or mazes in order to minimise larger deviations from intended and predictable walking patterns. In this paper, we present a novel approach for short-term path prediction which can be applied to virtual open space predictive redirected walking. We introduce a drop-shaped trajectory prediction which is described using a Lemniscate of Bernoulli. The drop's contour is discretised and we show how this is used to determine potential user trajectories in the virtual environment.