MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion

• URL: http://arxiv.org/abs/2306.03083v1
• Date: Mon, 5 Jun 2023 17:55:52 GMT
• Title: MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion
• Authors: Chiyu Max Jiang, Andre Cornman, Cheolho Park, Ben Sapp, Yin Zhou, Dragomir Anguelov
• Abstract summary: MotionDiffuser is a diffusion based representation for the joint distribution of future trajectories over multiple agents. We propose a general constrained sampling framework that enables controlled trajectory sampling based on differentiable cost functions. We obtain state-of-the-art results for multi-agent motion prediction on the Open Motion dataset.
• Score: 15.146808801331774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Deciphering Movement: Unified Trajectory Generation Model for Multi-Agent [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs. Specifically, we introduce a Ghost Spatial Masking (GSM) module embedded within a Transformer encoder for spatial feature extraction. We benchmark three practical sports game datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• Mixed Gaussian Flow for Diverse Trajectory Prediction [78.00204650749453]
  We propose a flow-based model to transform a mixed Gaussian prior into the future trajectory manifold. The model shows a better capacity for generating diverse trajectory patterns. We also demonstrate that it can generate diverse, controllable, and out-of-distribution trajectories.
  arXiv  Detail & Related papers  (2024-02-19T15:48:55Z)
• MotionLM: Multi-Agent Motion Forecasting as Language Modeling [15.317827804763699]
  We present MotionLM, a language model for multi-agent motion prediction. Our approach bypasses post-hoc interactions where individual agent trajectory generation is conducted prior to interactive scoring. The model's sequential factorization enables temporally causal conditional rollouts.
  arXiv  Detail & Related papers  (2023-09-28T15:46:25Z)
• JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving [12.460224193998362]
  We propose an end-to-end trainable model that learns directly the interaction between pairs of agents in a structured, graphical model formulation. Our approach improves significantly on the trajectory overlap metrics while obtaining on-par or better performance on single-agent trajectory metrics.
  arXiv  Detail & Related papers  (2022-12-16T20:59:21Z)
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
  We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID) We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-25T16:59:08Z)
• THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling [2.424910201171407]
  We present a unified model architecture for fast and simultaneous agent future heatmap estimation. generating scene-consistent predictions goes beyond the mere generation of collision-free trajectories. We report our results on the Interaction multi-agent prediction challenge and rank $1st$ on the online test leaderboard.
  arXiv  Detail & Related papers  (2021-10-13T10:05:47Z)
• Comparing Probability Distributions with Conditional Transport [63.11403041984197]
  We propose conditional transport (CT) as a new divergence and approximate it with the amortized CT (ACT) cost. ACT amortizes the computation of its conditional transport plans and comes with unbiased sample gradients that are straightforward to compute. On a wide variety of benchmark datasets generative modeling, substituting the default statistical distance of an existing generative adversarial network with ACT is shown to consistently improve the performance.
  arXiv  Detail & Related papers  (2020-12-28T05:14:22Z)
• Diverse Sampling for Normalizing Flow Based Trajectory Forecasting [34.01303881881315]
  We propose Diversity Sampling for Flow (DSF) to improve the quality and diversity of trajectory samples from a pre-trained flow model. DSF is easy to implement, and we show that it offers a simple plug-in improvement for several existing flow-based forecasting models.
  arXiv  Detail & Related papers  (2020-11-30T18:23:29Z)
• Haar Wavelet based Block Autoregressive Flows for Trajectories [129.37479472754083]
  Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. We introduce a novel Haar wavelet based block autoregressive model leveraging split couplings. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets.
  arXiv  Detail & Related papers  (2020-09-21T13:57:10Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.