Trajectory Prediction via Velocity Profile-Based Dynamic Model Selection

Abstract

Safe decision making in autonomous driving relies on accurately predicting the future positions of traffic actors. Although existing trajectory prediction methods mainly focus on map information and social interactions, actors’ intrinsic motion dynamics are often overlooked. In this study, we propose a dynamic trajectory prediction architecture based on velocity profiles extracted from observed past trajectories. The proposed architecture decomposes driving scenes into low- and high-velocity models and routes each sample to the corresponding expert model. Experiments on a large-scale real-world autonomous driving dataset show that velocity-based decomposition reduces prediction error compared with conventional single-model baselines.