Exploring Entropy-Based Solutions for Trajectory Prediction in Virtual Reality

This work explores the potential of entropy-based metrics to enhance the prediction of user navigation in Virtual Reality (VR). Specifically, we consider three entropy-based metrics: entropy of trajectories, which measures the overall variability and predictability of user behaviour; instantaneous entropy, which provides real-time assessments of movement predictability; and entropy of saliency maps, which offers insights into content-driven attention patterns. Through an exploratory behavioural analysis, we show that users with low entropy exhibit consistent and predictable navigation patterns, while high-entropy users pose greater challenges for prediction models. Building on these findings, we introduce three novel entropy-based solutions for VR trajectory prediction: a position-only baseline augmented with entropy information, an LSTM-based architecture with an entropy-based adaptive attention layer (E-AALSTM), and a multi-head attention-based architecture with adaptive attention (AMH). The proposed models performs as good as state-of-the-art methods, while improving stability and robustness in specific scenarios. This work highlights the importance of having an holistic metric to characterise the user behaviour in VR, and thus enhance trajectory prediction frameworks.