Paper · arXiv 2306.03083
MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion
Chiyu Max Jiang, Andre Cornman, Cheolho Park, Ben Sapp, Yin Zhou, Dragomir Anguelov
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.
Abstract
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.