Trajectory prediction plays a key role in autonomous driving and intelligent transportation systems. Mamba performs well in modeling long sequences but struggles with short-term static or local motion features. In this paper, we propose the Low-rank Recursive Mamba framework (Los-Mamba). It addresses prediction bias caused by inadequate modeling of local static features in trajectory prediction tasks. Firstly, Los-Mamba uses hybrid Mamba branches to capture both global motion trends and local features. Secondly, it reduces trajectory feature dimensionality through low-rank approximation, simplifying computational complexity. Thirdly, Los-Mamba also includes a spatio-temporal constraint mechanism. It uses position coding and a multilayer perceptron (MLP) to interpolate static and dynamic trajectories, improving the representation of local features. Experiments show that Los-Mamba outperforms baseline models on datasets like ETH and Hotel. Its average displacement error (ADE) and final displacement error (FDE) are reduced by up to 38.3% and 40.5%, respectively. This work offers an efficient solution for trajectory prediction in complex, dynamic environments.
