Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling

• URL: http://arxiv.org/abs/2503.18589v2
• Date: Sat, 29 Mar 2025 11:06:03 GMT
• Title: Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling
• Authors: Guillem Capellera, Antonio Rubio, Luis Ferraz, Antonio Agudo,
• Abstract summary: We introduce U2Diff, a textbfunified diffusion model designed to handle trajectory completion.<n>We also incorporate a Rank Neural Network in post-processing to enable textbferror probability estimation for each generated mode.<n>Our method outperforms the state-of-the-art solutions in trajectory completion and forecasting across four challenging sports datasets.
• Score: 13.993231805213354
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-agent trajectory modeling has primarily focused on forecasting future states, often overlooking broader tasks like trajectory completion, which are crucial for real-world applications such as correcting tracking data. Existing methods also generally predict agents' states without offering any state-wise measure of uncertainty. Moreover, popular multi-modal sampling methods lack any error probability estimates for each generated scene under the same prior observations, making it difficult to rank the predictions during inference time. We introduce U2Diff, a \textbf{unified} diffusion model designed to handle trajectory completion while providing state-wise \textbf{uncertainty} estimates jointly. This uncertainty estimation is achieved by augmenting the simple denoising loss with the negative log-likelihood of the predicted noise and propagating latent space uncertainty to the real state space. Additionally, we incorporate a Rank Neural Network in post-processing to enable \textbf{error probability} estimation for each generated mode, demonstrating a strong correlation with the error relative to ground truth. Our method outperforms the state-of-the-art solutions in trajectory completion and forecasting across four challenging sports datasets (NBA, Basketball-U, Football-U, Soccer-U), highlighting the effectiveness of uncertainty and error probability estimation. Video at https://youtu.be/ngw4D4eJToE

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