ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties

• URL: http://arxiv.org/abs/2405.00797v1
• Date: Wed, 1 May 2024 18:16:55 GMT
• Title: ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties
• Authors: Jiahui Li, Tianle Shen, Zekai Gu, Jiawei Sun, Chengran Yuan, Yuhang Han, Shuo Sun, Marcelo H. Ang Jr,
• Abstract summary: We propose a novel diffusion-based, acceleratable framework that adeptly predicts future trajectories of agents with enhanced resistance to noise. Our method meets the rigorous real-time operational standards essential for autonomous vehicles. It achieves significant improvement in multi-agent motion prediction on the Argoverse 1 motion forecasting dataset.
• Score: 6.865435680843742
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Motion prediction is a challenging problem in autonomous driving as it demands the system to comprehend stochastic dynamics and the multi-modal nature of real-world agent interactions. Diffusion models have recently risen to prominence, and have proven particularly effective in pedestrian motion prediction tasks. However, the significant time consumption and sensitivity to noise have limited the real-time predictive capability of diffusion models. In response to these impediments, we propose a novel diffusion-based, acceleratable framework that adeptly predicts future trajectories of agents with enhanced resistance to noise. The core idea of our model is to learn a coarse-grained prior distribution of trajectory, which can skip a large number of denoise steps. This advancement not only boosts sampling efficiency but also maintains the fidelity of prediction accuracy. Our method meets the rigorous real-time operational standards essential for autonomous vehicles, enabling prompt trajectory generation that is vital for secure and efficient navigation. Through extensive experiments, our method speeds up the inference time to 136ms compared to standard diffusion model, and achieves significant improvement in multi-agent motion prediction on the Argoverse 1 motion forecasting dataset.

Related papers

• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models [11.308331231957588]
  This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a novel network architecture for highway trajectory prediction using diffusion models. Central to the architecture of cVMD is its capacity to perform uncertainty quantification, a feature that is crucial in safety-critical applications. Experiments show that the proposed architecture achieves competitive trajectory prediction accuracy compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-05-23T10:01:39Z)
• Multi-agent Traffic Prediction via Denoised Endpoint Distribution [23.767783008524678]
  Trajectory prediction at high speeds requires historical features and interactions with surrounding entities. We present the Denoised Distribution model for trajectory prediction. Our approach significantly reduces model complexity and performance through endpoint information.
  arXiv  Detail & Related papers  (2024-05-11T15:41:32Z)
• Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations [53.797896854533384]
  Class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning, the manual labeling of point cloud data is laborious and time-consuming. We introduce three simple spatial and temporal regularization losses, which facilitate the self-supervised training process effectively.
  arXiv  Detail & Related papers  (2024-03-20T02:58:45Z)
• GBD-TS: Goal-based Pedestrian Trajectory Prediction with Diffusion using Tree Sampling Algorithm [18.367711156885203]
  We propose a novel scene-aware multi-modal pedestrian trajectory prediction framework called GBD. First, the goal predictor produces multiple goals, and then the diffusion network generates multi-modal trajectories conditioned on these goals.
  arXiv  Detail & Related papers  (2023-11-25T03:55:06Z)
• DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction [7.346307332191997]
  We present a novel framework that leverages diffusion models for predicting future trajectories in a computationally efficient manner. We employ an efficient sampling mechanism that allows us to maximize the number of sampled trajectories for improved accuracy. We show the effectiveness of our approach by conducting empirical evaluations on common pedestrian (UCY/ETH) and autonomous driving (nuScenes) benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-23T05:04:23Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Bootstrap Motion Forecasting With Self-Consistent Constraints [52.88100002373369]
  We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints. The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past. We show that our proposed scheme consistently improves the prediction performance of several existing methods.
  arXiv  Detail & Related papers  (2022-04-12T14:59:48Z)
• 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)
• FloMo: Tractable Motion Prediction with Normalizing Flows [0.0]
  We model motion prediction as a density estimation problem with a normalizing flow between a noise sample and the future motion distribution. Our model, named FloMo, allows likelihoods to be computed in a single network pass and can be trained directly with maximum likelihood estimation. Our method achieves state-of-the-art performance on three popular prediction datasets, with a significant gap to most competing models.
  arXiv  Detail & Related papers  (2021-03-05T11:35:27Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

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.