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.
Abstract (translated)
运动预测是自动驾驶中的一个具有挑战性的问题，因为它要求系统理解随机动态和真实世界中代理互动的多模态性质。扩散模型最近受到了关注，并在行人运动预测任务中表现尤为有效。然而，显著的运行时间和对噪声的敏感性限制了扩散模型的实时预测能力。为了应对这些障碍，我们提出了一个新型的扩散-基于，加速的框架，能够有效地预测具有抗噪能力的未来轨迹。我们模型的核心思想是学习轨迹的粗粒度先验分布，可以跳过大量去噪步骤。这一进步不仅提高了抽样效率，还保持了预测精度的准确性。我们的方法满足自动驾驶车辆所需的严格实时运行标准，能够快速生成安全高效的轨迹。通过大量实验，我们的方法将标准扩散模型的推理时间缩短至136ms，同时在Argoverse 1运动预测数据集上显著提高了多代理器运动预测的准确性。
URL
https://arxiv.org/abs/2405.00797
