ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties

• URL: http://arxiv.org/abs/2405.00797v1
• Date: Wed, 1 May 2024 18:16:55 GMT
• Title: ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties
• Authors: Jiahui Li, Tianle Shen, Zekai Gu, Jiawei Sun, Chengran Yuan, Yuhang Han, Shuo Sun, Marcelo H. Ang Jr,
• Abstract summary: We propose a novel diffusion-based, acceleratable framework that adeptly predicts future trajectories of agents with enhanced resistance to noise. Our method meets the rigorous real-time operational standards essential for autonomous vehicles. It achieves significant improvement in multi-agent motion prediction on the Argoverse 1 motion forecasting dataset.
• Score: 6.865435680843742
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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