MotionDiffuser: Controllable Multi-Agent Motion Prediction using
  Diffusion
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.
647eb52fcfca67bc5000017a
Chiyu Max Jiang
647eb52fcfca67bc5000017b
Andre Cornman
647eb52fcfca67bc5000017c
Cheolho Park
647eb52fcfca67bc5000017d
Ben Sapp
647eb52fcfca67bc5000017e
Yin Zhou
647eb52fcfca67bc5000017f
Dragomir Anguelov
MotionDiffuser: Controllable Multi-Agent Motion Prediction using
  Diffusion
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.
MotionDiffuser, a diffusion model for multi-agent trajectory prediction, achieves state-of-the-art results by learning a multimodal distribution with a simple predictor and permutation-invariant joint learning, using PCA for trajectory compression and differentiable cost functions for constrained sampling.
diffusion based representation multimodal distribution trajectory anchors permutation-invariant PCA compressed trajectory representation exact sample log probability constrained sampling differentiable cost functions multi-agent motion prediction Waymo Open Motion Dataset