Neural ODE Network-Based Dynamic Object Trajectory Prediction

Trajectory prediction for dynamic objects plays a crucial role in predicting spatiotemporal motion patterns in complex dynamic environments, with significant implications for robotics and motion control systems. Conventional approaches often struggle to effectively capture complex nonlinear dynamics, resulting in compromised prediction accuracy and generalization capability. This paper proposes a Neural Ordinary Differential Equation (Neural ODE)-based framework for dynamic trajectory prediction. This method, which seamlessly integrates physical principles into the formulations of differential equations while maintaining compatible with deep neural networks, enables a more robust and accurate characterization of intricate motion patterns during dynamic processes. Experimental results demonstrate that the proposed method ensures real-time prediction efficiency while exhibiting robust generalization capabilities across diverse scenarios. This advancement provides a reliable solution for dynamic trajectory prediction, potentially facilitating technological developments in related fields.