  Motion prediction is a challenging problem in autonomous driving as it
demands the system to comprehend stochastic dynamics and the multi-modal nature
of real-world agent interactions. Diffusion models have recently risen to
prominence, and have proven particularly effective in pedestrian motion
prediction tasks. However, the significant time consumption and sensitivity to
noise have limited the real-time predictive capability of diffusion models. In
response to these impediments, we propose a novel diffusion-based,
acceleratable framework that adeptly predicts future trajectories of agents
with enhanced resistance to noise. The core idea of our model is to learn a
coarse-grained prior distribution of trajectory, which can skip a large number
of denoise steps. This advancement not only boosts sampling efficiency but also
maintains the fidelity of prediction accuracy. Our method meets the rigorous
real-time operational standards essential for autonomous vehicles, enabling
prompt trajectory generation that is vital for secure and efficient navigation.
Through extensive experiments, our method speeds up the inference time to 136ms
compared to standard diffusion model, and achieves significant improvement in
multi-agent motion prediction on the Argoverse 1 motion forecasting dataset.
