Collaborative-Distilled Diffusion Models (CDDM) for Accelerated and Lightweight Trajectory Prediction

• URL: http://arxiv.org/abs/2510.00627v1
• Date: Wed, 01 Oct 2025 08:00:31 GMT
• Title: Collaborative-Distilled Diffusion Models (CDDM) for Accelerated and Lightweight Trajectory Prediction
• Authors: Bingzhang Wang, Kehua Chen, Yinhai Wang,
• Abstract summary: Trajectory prediction is a fundamental task in Autonomous Vehicles (AVs) and Intelligent Transportation Systems (ITS)<n> Diffusion models have recently demonstrated strong performance in probabilistic trajectory prediction.<n>This paper proposes Collaborative-Distilled Diffusion Models (CDDM), a novel method for real-time and lightweight trajectory prediction.
• Score: 14.108460337857645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory prediction is a fundamental task in Autonomous Vehicles (AVs) and Intelligent Transportation Systems (ITS), supporting efficient motion planning and real-time traffic safety management. Diffusion models have recently demonstrated strong performance in probabilistic trajectory prediction, but their large model size and slow sampling process hinder real-world deployment. This paper proposes Collaborative-Distilled Diffusion Models (CDDM), a novel method for real-time and lightweight trajectory prediction. Built upon Collaborative Progressive Distillation (CPD), CDDM progressively transfers knowledge from a high-capacity teacher diffusion model to a lightweight student model, jointly reducing both the number of sampling steps and the model size across distillation iterations. A dual-signal regularized distillation loss is further introduced to incorporate guidance from both the teacher and ground-truth data, mitigating potential overfitting and ensuring robust performance. Extensive experiments on the ETH-UCY pedestrian benchmark and the nuScenes vehicle benchmark demonstrate that CDDM achieves state-of-the-art prediction accuracy. The well-distilled CDDM retains 96.2% and 95.5% of the baseline model's ADE and FDE performance on pedestrian trajectories, while requiring only 231K parameters and 4 or 2 sampling steps, corresponding to 161x compression, 31x acceleration, and 9 ms latency. Qualitative results further show that CDDM generates diverse and accurate trajectories under dynamic agent behaviors and complex social interactions. By bridging high-performing generative models with practical deployment constraints, CDDM enables resource-efficient probabilistic prediction for AVs and ITS. Code is available at https://github.com/bingzhangw/CDDM.

Related papers

• Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling [14.988778271653038]
  cVMDx is a diffusion-based trajectory prediction framework that improves efficiency, robustness and multimodal predictive capability.<n> DDIM sampling achieves up to a 100x reduction in inference time, enabling practical multi-sample generation for uncertainty estimation.<n>Experiments show that cVMDx achieves higher accuracy and significantly improved efficiency over cVMD, enabling fully multimodal trajectory prediction.
  arXiv  Detail & Related papers  (2026-02-24T19:40:37Z)
• Efficient UAV trajectory prediction: A multi-modal deep diffusion framework [26.678930486634602]
  A multi-modal UAV trajectory prediction method based on the fusion of LiDAR and millimeter-wave radar information is proposed.<n>The proposed model can effectively utilize multi-modal data and provides an efficient solution for unauthorized UAV trajectory prediction in the low-altitude economy.
  arXiv  Detail & Related papers  (2026-01-26T13:14:52Z)
• Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models [100.28111930893188]
  Some of today's best generative models still require hundreds to thousands of neural function evaluations to compute a single likelihood.<n>We present fast flow joint distillation (F2D2), a framework that simultaneously reduces the number of NFEs required for both sampling and likelihood evaluation by two orders of magnitude.<n>F2D2 is modular, compatible with existing flow-based few-step sampling models, and requires only an additional divergence prediction head.
  arXiv  Detail & Related papers  (2025-12-02T10:48:20Z)
• Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals [48.14879329270912]
  Phased DMD is a multi-step distillation framework that bridges the idea of phase-wise distillation with Mixture-of-Experts.<n>Phased DMD is built upon two key ideas: progressive distribution matching and score matching within subintervals.<n> Experimental results demonstrate that Phased DMD preserves output diversity better than DMD while retaining key generative capabilities.
  arXiv  Detail & Related papers  (2025-10-31T17:55:10Z)
• SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation [62.14510717860079]
  We propose a Synergistic Diffusion-Autoregression paradigm that unifies the training efficiency of autoregressive models with the parallel inference capability of diffusion.<n>SDAR performs a lightweight paradigm conversion that transforms a well-trained autoregressive (AR) model into a blockwise diffusion model through brief, data-efficient adaptation.<n>Building on this insight, SDAR achieves efficient AR-to-diffusion conversion with minimal cost, preserving AR-level performance while enabling parallel generation.
  arXiv  Detail & Related papers  (2025-10-07T17:29:28Z)
• Improved Diffusion-based Generative Model with Better Adversarial Robustness [65.38540020916432]
  Diffusion Probabilistic Models (DPMs) have achieved significant success in generative tasks.<n>During the denoising process, the input data distributions differ between the training and inference stages.
  arXiv  Detail & Related papers  (2025-02-24T12:29:16Z)
• 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)
• 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)
• GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction [15.731398013255179]
  We propose a novel Goal-Guided Diffusion Model with Tree Sampling for multi-modal trajectory prediction.<n>A two-stage tree sampling algorithm is presented, which leverages common features to reduce the inference time and improve accuracy for multi-modal prediction.<n> Experimental results demonstrate that our proposed framework achieves comparable state-of-the-art performance with real-time inference speed in public datasets.
  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)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion [56.38386580040991]
  Consistency Trajectory Model (CTM) is a generalization of Consistency Models (CM) CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance. Unlike CM, CTM's access to the score function can streamline the adoption of established controllable/conditional generation methods.
  arXiv  Detail & Related papers  (2023-10-01T05:07:17Z)

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.