Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models

Marion Neumeier, Sebastian Dorn, Michael Botsch, Wolfgang Utschick

TL;DR

This paper tackles the challenge of highway trajectory prediction by guaranteeing drivability and incorporating uncertainty quantification. It introduces conditioned Vehicle Motion Diffusion (cVMD), which combines a VQ-VAE-based context encoder with a diffusion-based trajectory generator that operates under non-holonomic vehicle constraints and a vehicle motion model. A key contribution is the integration of an uncertainty-aware guidance mechanism, where the diffusion conditioning scale adapts to observed model confidence, enabling both accurate predictions and informative uncertainty intervals. The approach achieves competitive performance on the highD dataset while providing guaranteed drivable trajectories and a principled way to quantify and leverage prediction uncertainty for safer autonomous driving decisions.

Abstract

This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a novel network architecture for highway trajectory prediction using diffusion models. The proposed model ensures the drivability of the predicted trajectory by integrating non-holonomic motion constraints and physical constraints into the generative prediction module. Central to the architecture of cVMD is its capacity to perform uncertainty quantification, a feature that is crucial in safety-critical applications. By integrating the quantified uncertainty into the prediction process, the cVMD's trajectory prediction performance is improved considerably. The model's performance was evaluated using the publicly available highD dataset. Experiments show that the proposed architecture achieves competitive trajectory prediction accuracy compared to state-of-the-art models, while providing guaranteed drivable trajectories and uncertainty quantification.

Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models

TL;DR

This paper tackles the challenge of highway trajectory prediction by guaranteeing drivability and incorporating uncertainty quantification. It introduces conditioned Vehicle Motion Diffusion (cVMD), which combines a VQ-VAE-based context encoder with a diffusion-based trajectory generator that operates under non-holonomic vehicle constraints and a vehicle motion model. A key contribution is the integration of an uncertainty-aware guidance mechanism, where the diffusion conditioning scale adapts to observed model confidence, enabling both accurate predictions and informative uncertainty intervals. The approach achieves competitive performance on the highD dataset while providing guaranteed drivable trajectories and a principled way to quantify and leverage prediction uncertainty for safer autonomous driving decisions.

Abstract

This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a novel network architecture for highway trajectory prediction using diffusion models. The proposed model ensures the drivability of the predicted trajectory by integrating non-holonomic motion constraints and physical constraints into the generative prediction module. Central to the architecture of cVMD is its capacity to perform uncertainty quantification, a feature that is crucial in safety-critical applications. By integrating the quantified uncertainty into the prediction process, the cVMD's trajectory prediction performance is improved considerably. The model's performance was evaluated using the publicly available highD dataset. Experiments show that the proposed architecture achieves competitive trajectory prediction accuracy compared to state-of-the-art models, while providing guaranteed drivable trajectories and uncertainty quantification.
Paper Structure (19 sections, 16 equations, 5 figures, 2 tables)

This paper contains 19 sections, 16 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Denoising Diffusion Probabilistic Model
  5. Classifier-Free Guidance
  6. Method
  7. Problem Formulation
  8. Context Conditioning
  9. Vehicle Motion Diffusion (VMD)
  10. DDPM
  11. Vehicle Motion Model (VMM)
  12. Uncertainty Quantification
  13. Uncertainty-adaptive Guidance Scale
  14. Dataset and Experiments
  15. Evaluation
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Architecture of cVMD, composed of three primary components: vehicle motion diffusion module, context conditioning module, and uncertainty quantification unit (UQ). The context conditioning module, realized as VQ-VAE, discretizes the traffic scenario $\boldsymbol{\xi}$. The index $q$ of the discretized scenario context is then passed to the diffusion model as condition $c$. UQ determines the prediction uncertainty, which is used to adaptively modify the guidance scale $w$ of the vehicle motion diffusion module used for the trajectory prediction.
  • Figure 2: Diffusion processes transform (a) data $p(x_0)$ into to Gaussian noise (b) $p(x_T)$ (\ref{['stdplot']}). While the distribution of positional data (\ref{['distPos']}) is highly depending on the map data, the motion parameter distribution (\ref{['distOP']}) is more likely to resemble a Gaussian distribution.
  • Figure 3: Example of univariate MLE based on a set of samples $\mathcal{H}_1$(\ref{['hq1']}) and $\mathcal{H}_2$(\ref{['hq2']}). A new data sample (\ref{['new_sample']}) is assigned to $q_2$ with the quantified uncertainty $\delta_m$.
  • Figure 4: Stacked histogram for the selected context condition $q$ from the codebook. The histogram has a logarithmic scale.
  • Figure 5: Trajectory prediction $\bm{\mu}_p$ (\ref{['mean']}) with confidence intervals $\bm{\sigma}_p$ (\ref{['sigma1']}), $2\bm{\sigma}_p$ (\ref{['sigma2']}), $3\bm{\sigma}_p$ (\ref{['sigma3']}) for a scenario assigned to index $q=4$ and the ground truth trajectory (\ref{['gt']}).