推定事前分布による拡散モデルの高速化：不確実性下でのロバストな運動予測
ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties
拡散モデルに粗い事前分布を学習させてデノイズステップを削減し、自動運転の運動予測を136msで高速・高精度に実現した研究。
著者: Jiahui Li, Tianle Shen, Zekai Gu, Jiawei Sun, Chengran Yuan, Yuhang Han, Shuo Sun, Marcelo H. Ang
分類: cs.RO, cs.CV
原文アブストラクト
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.
