Stochastic-Aware Mamba Diffusion for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction plays a crucial role in understanding human behavior and intentions. Due to the inherent randomness in human movement, current research constructs trajectories in stochastic space and uses diffusion models to reverse the denoising process. The commonly used denoising model, Transformer, is affected by uneven noise sampling, leading to confusion between temporal consistency and randomness across multiple time points. To address this issue, we propose a novel framework named Stochastic-Aware Mamba Diffusion (SAMD), which combines Stochastic-Aware Mamba with the Diffusion model to predict motion noise and motion states in the stochastic space. It utilizes a temporal aggregator to extract temporal consistency features. We construct a motion-selective state space model, which includes the adaptive transition between consistency motion states and stochastic states for balancing stability and diversity. The motion gating unit activates pertinent information within motion states to extract motion noise for trajectory prediction. Our approach achieves state-of-the-art results on the ETH-UCY and SDD datasets while significantly reducing the computational cost.