GBD-TS: Goal-based Pedestrian Trajectory Prediction with Diffusion using Tree Sampling Algorithm

• URL: http://arxiv.org/abs/2311.14922v1
• Date: Sat, 25 Nov 2023 03:55:06 GMT
• Title: GBD-TS: Goal-based Pedestrian Trajectory Prediction with Diffusion using Tree Sampling Algorithm
• Authors: Ge Sun, Sheng Wang, Yang Xiao, Lei Zhu, Ming Liu
• Abstract summary: 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.
• Score: 18.367711156885203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting pedestrian trajectories is crucial for improving the safety and effectiveness of autonomous driving and mobile robots. However, this task is nontrivial due to the inherent stochasticity of human motion, which naturally requires the predictor to generate multi-model prediction. Previous works have used various generative methods, such as GAN and VAE, for pedestrian trajectory prediction. Nevertheless, these methods may suffer from problems, including mode collapse and relatively low-quality results. The denoising diffusion probabilistic model (DDPM) has recently been applied to trajectory prediction due to its simple training process and powerful reconstruction ability. However, current diffusion-based methods are straightforward without fully leveraging input information and usually require many denoising iterations leading to a long inference time or an additional network for initialization. To address these challenges and promote the application of diffusion models in trajectory prediction, we propose a novel scene-aware multi-modal pedestrian trajectory prediction framework called GBD. GBD combines goal prediction with the diffusion network. First, the goal predictor produces multiple goals, and then the diffusion network generates multi-modal trajectories conditioned on these goals. Furthermore, we introduce a new diffusion sampling algorithm named tree sampling (TS), which leverages common feature to reduce the inference time and improve accuracy for multi-modal prediction. Experimental results demonstrate that our GBD-TS method achieves state-of-the-art performance with real-time inference speed.

Related papers

• Intention Enhanced Diffusion Model for Multimodal Pedestrian Trajectory Prediction [15.151965172049271]
  Predicting pedestrian motion trajectories is critical for path planning and motion control of autonomous vehicles.<n>Recent diffusion-based models have shown promising results in capturing theity of pedestrian behavior for trajectory prediction.<n>We propose a diffusion-based multimodal trajectory prediction model that incorporates pedestrians' motion intentions into the prediction framework.
  arXiv  Detail & Related papers  (2025-08-06T09:04:54Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• TrajFlow: Multi-modal Motion Prediction via Flow Matching [29.274577509291973]
  We introduce TrajFlow, a novel flow matching-based motion prediction framework.<n>TrajFlow predicts multiple plausible future trajectories in a single pass, significantly reducing computational overhead.<n>It achieves state-of-the-art performance across various key metrics, underscoring its effectiveness for safety-critical autonomous driving applications.
  arXiv  Detail & Related papers  (2025-06-10T08:08:31Z)
• Consistent World Models via Foresight Diffusion [56.45012929930605]
  We argue that a key bottleneck in learning consistent diffusion-based world models lies in the suboptimal predictive ability.<n>We propose Foresight Diffusion (ForeDiff), a diffusion-based world modeling framework that enhances consistency by decoupling condition understanding from target denoising.
  arXiv  Detail & Related papers  (2025-05-22T10:01:59Z)
• Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation [49.49868273653921]
  Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving. We introduce Optimal Gaussian Diffusion (OGD) and Estimated Clean Manifold (ECM) Guidance. Our methodology streamlines the generative process, enabling practical applications with reduced computational overhead.
  arXiv  Detail & Related papers  (2024-08-01T17:59:59Z)
• ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties [6.865435680843742]
  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.
  arXiv  Detail & Related papers  (2024-05-01T18:16:55Z)
• Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach [54.429396802848224]
  This paper proposes an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves the target-driven motion prediction by estimating the spatial distribution of long-term destinations. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable.
  arXiv  Detail & Related papers  (2024-03-10T04:16:04Z)
• MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process [26.661721555671626]
  We introduce a novel Multi-Granularity Time Series (MG-TSD) model, which achieves state-of-the-art predictive performance. Our approach does not rely on additional external data, making it versatile and applicable across various domains.
  arXiv  Detail & Related papers  (2024-03-09T01:15:03Z)
• 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)
• Leapfrog Diffusion Model for Stochastic Trajectory Prediction [32.36667797656046]
  We present LEapfrog Diffusion model (LED), a novel diffusion-based trajectory prediction model. LED provides real-time, precise, and diverse predictions. LED consistently improves performance and achieves 23.7%/21.9% ADE/FDE improvement on NFL.
  arXiv  Detail & Related papers  (2023-03-20T06:32:48Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• 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)
• 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)
• Adversarial Refinement Network for Human Motion Prediction [61.50462663314644]
  Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend. We propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation.
  arXiv  Detail & Related papers  (2020-11-23T05:42:20Z)
• 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.