# F-RDW: Redirected Walking with Forecasting Future Position

arXiv: https://arxiv.org/abs/2304.03497 · HTML: https://mlsanity.com/paper/2304.03497/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 83 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data](https://mlsanity.com/paper/2012.15441/) (2020)
- [Redirected Walking in Static and Dynamic Scenes Using Visibility Polygons](https://mlsanity.com/paper/2106.06807/) (2021)
- [Redirection Controller Using Reinforcement Learning](https://mlsanity.com/paper/1909.09505/) (2019)
- [ARC: Alignment-based Redirection Controller for Redirected Walking in Complex Environments](https://mlsanity.com/paper/2101.04912/) (2021)
- [ENI: Quantifying Environment Compatibility for Natural Walking in Virtual Reality](https://mlsanity.com/paper/2201.01261/) (2022)
- [Effects of Virtual Room Size and Objects on Relative Translation Gain Thresholds in Redirected Walking](https://mlsanity.com/paper/2201.04273/) (2022)
- [Short-Term Trajectory Prediction for Full-Immersive Multiuser Virtual Reality with Redirected Walking](https://mlsanity.com/paper/2207.07520/) (2022)

## Built on by (most cited first, 1 of 1)

- [Eye Gaze-Informed and Context-Aware Pedestrian Trajectory Prediction in Shared Spaces with Automated Shuttles: A Virtual Reality Study](https://mlsanity.com/paper/2603.19812/) (2026)

## Abstract (arXiv)

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.