Integrating deep learning model and virtual reality technology for motion prediction in emergencies

Predicting the evacuation behavior of pedestrians in emergencies is essential for ensuring public safety. Existing deep learning-based prediction models generally focus on crowd trajectories extrapolation in conventional scenarios but ignore the effect of emergencies on human behavior. Their performance has not been rigorously validated during emergency events such as fires and floodwaters. In this paper, we implement a combined solution involving a transformer-based network and virtual reality (VR) modeling. The proposed virtual reality-trained neural network incorporates diverse cues from human poses, moving paths, scenes, and emergency events to predict future trajectories. The virtual reality modeling creates diverse evacuation scenarios to enhance prediction performance. Moreover, based on the pretraining of our constructed VR dataset, the designed model can be applied to real-world human behavior prediction. The experimental results demonstrate our model’s superior accuracy in various scenarios, particularly for emergency evacuations, showcasing its ability to capture the dynamics of human behavior in safety-critical environments.