MotionDiffuser：使用扩散实现可控的多智能体运动预测

MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion

June 5, 2023

作者: Chiyu Max Jiang, Andre Cornman, Cheolho Park, Ben Sapp, Yin Zhou, Dragomir Anguelov

cs.AI

摘要

我们提出了MotionDiffuser，这是一种基于扩散的表示方法，用于描述多个智能体未来轨迹的联合分布。这种表示具有几个关键优势：首先，我们的模型学习到了一个能够捕捉多样化未来结果的高度多模态分布。其次，简单的预测器设计仅需要一个单一的L2损失训练目标，并且不依赖于轨迹锚点。第三，我们的模型能够以置换不变的方式学习多个智能体运动的联合分布。此外，我们利用PCA实现了压缩轨迹表示，提高了模型性能，并实现了精确样本对数概率的高效计算。随后，我们提出了一个通用的受限采样框架，基于可微成本函数实现了受控轨迹采样。这种策略可以实现一系列应用，如强制规则和物理先验，或创建定制仿真场景。MotionDiffuser可以与现有的主干架构结合，实现最佳的运动预测结果。我们在Waymo开放运动数据集上获得了多智能体运动预测的最新成果。

English

We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimodal distribution that captures diverse future outcomes. Second, the simple predictor design requires only a single L2 loss training objective, and does not depend on trajectory anchors. Third, our model is capable of learning the joint distribution for the motion of multiple agents in a permutation-invariant manner. Furthermore, we utilize a compressed trajectory representation via PCA, which improves model performance and allows for efficient computation of the exact sample log probability. Subsequently, we propose a general constrained sampling framework that enables controlled trajectory sampling based on differentiable cost functions. This strategy enables a host of applications such as enforcing rules and physical priors, or creating tailored simulation scenarios. MotionDiffuser can be combined with existing backbone architectures to achieve top motion forecasting results. We obtain state-of-the-art results for multi-agent motion prediction on the Waymo Open Motion Dataset.