title: MotionDiffuser: Controllable Multi-Agent Motion Prediction using
    Diffusion
summary: 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.
paper_authors._id: 647eb52fcfca67bc5000017a
paper_authors.name: Chiyu Max Jiang
paper_authors._id: 647eb52fcfca67bc5000017b
paper_authors.name: Andre Cornman
paper_authors._id: 647eb52fcfca67bc5000017c
paper_authors.name: Cheolho Park
paper_authors._id: 647eb52fcfca67bc5000017d
paper_authors.name: Ben Sapp
paper_authors._id: 647eb52fcfca67bc5000017e
paper_authors.name: Yin Zhou
paper_authors._id: 647eb52fcfca67bc5000017f
paper_authors.name: Dragomir Anguelov
paper_title: MotionDiffuser: Controllable Multi-Agent Motion Prediction using
    Diffusion
paper_summary: 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.
paper_ai_keywords: diffusion based representation multimodal distribution L2 loss training objective permutation-invariant PCA compressed trajectory representation exact sample log probability differentiable cost functions multi-agent motion prediction Waymo Open Motion Dataset