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.

本文提出了 MotionDiffuser 模型，其能够学习多个交通参与者之间联合分布的多模态表示，在实现高效计算精确样本概率的前提下，提出了一种压缩轨迹表示以提高模型性能，并结合约束抽样框架，以应用于强制束缚和创造定制化模拟场景等应用，最终在 Waymo 数据集上实现了最先进的多代理运动预测结果。