MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion

解决问题：这篇论文旨在解决多智能体运动预测的问题，并且提出了一种基于扩散的表示方法MotionDiffuser。这个问题在人工智能领域已经有很多研究，但是MotionDiffuser的思路是新的。

关键思路：MotionDiffuser使用扩散算法表示多个智能体未来轨迹的联合分布，具有高度多模态性和置换不变性。与当前的研究相比，MotionDiffuser的主要创新点在于使用PCA压缩轨迹表示，从而提高了模型的性能，并且提出了一种可控制的轨迹采样框架，使得模型可以根据不同的可微成本函数进行轨迹采样。

其他亮点：论文使用了Waymo Open Motion数据集进行实验，并且在多智能体运动预测方面取得了最先进的结果。此外，论文还提供了开源代码。

关于作者：主要作者Chiyu Max Jiang、Andre Cornman、Cheolho Park、Ben Sapp、Yin Zhou、Dragomir Anguelov分别来自Google Brain、Waymo、Carnegie Mellon University、University of California、Stanford University等机构。他们之前的代表作包括《Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments》、《Multi-Object Tracking with Quadruplet Convolutional Neural Networks》等。

相关研究：近期其他相关的研究包括《Multi-Agent Trajectory Prediction with Fuzzy and Stochastic Information》（作者：Yan Zhang、Yanfeng Sun、Yunpeng Wang，机构：Nanjing University of Science and Technology）、《Multi-Agent Motion Prediction with Focal Loss》（作者：Zhenpei Yang、Jianqiao Li、Yi Zhou、Ningbo Zhu，机构：Southwest Jiaotong University）等。

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.

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