DD-MDN: Human Trajectory Forecasting with Diffusion-Based Dual Mixture Density Networks and Uncertainty Self-Calibration

2026-02-13 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

DD-MDN is a novel end-to-end probabilistic Human Trajectory Forecasting (HTF) model that integrates a few-shot denoising diffusion backbone with a dual Mixture Density Network (MDN). This architecture generates self-calibrated residence areas and probability-ranked anchor paths, from which diverse trajectory hypotheses are derived without requiring predefined anchors or endpoints. The model addresses critical gaps in HTF, specifically focusing on robust uncertainty modeling, calibration, and accurate forecasts from short observation periods, which are vital for applications like autonomous driving and human-robot interaction. Experiments on ETH/UCY, SDD, inD, and IMPTC datasets demonstrate DD-MDN's state-of-the-art accuracy, particularly its robustness with short observation intervals (e.g., two frames), and its reliable uncertainty estimates. The model also boasts a compact size of 4.5 MB and an inference latency of 15.5 ms at a batch size of 64.

Key takeaway

For Computer Vision Engineers developing autonomous systems, DD-MDN offers a robust solution for human trajectory forecasting that provides both high accuracy and reliable uncertainty estimates, even with limited observation data. You should consider integrating this model to enhance path planning and collision avoidance, especially in scenarios requiring rapid decision-making from short input sequences. Its compact size and low latency also make it suitable for edge deployment.

Key insights

DD-MDN unifies multimodal accuracy with self-calibrated uncertainty in human trajectory forecasting, even with short observations.

Principles

• Calibrated uncertainty is crucial for downstream decision-making.
• NLL training enables self-calibration of aleatoric uncertainty.
• Denoising diffusion can regularize complex parameter manifolds.

Method

DD-MDN uses a few-shot denoising diffusion backbone and a dual MDN to generate two Gaussian Mixture representations: per-timestep and per-anchor-trajectory, optimized via NLL for self-calibrated uncertainty.

In practice

• Utilize dual GM representations for robust uncertainty.
• Employ dynamic input-horizon scaling for robustness.
• Consider FP16/FP8 for memory-constrained edge deployment.

Topics

• Human Trajectory Forecasting
• Diffusion Models
• Mixture Density Networks
• Uncertainty Calibration
• Probabilistic Forecasting

Code references

• kav-institute/ddmdn

Best for: Computer Vision Engineer, Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.