Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving, but they face challenges of inefficient inference time and high computational demands. To tackle these challenges, we introduce Optimal Gaussian Diffusion (OGD) and Estimated Clean Manifold (ECM) Guidance. OGD optimizes the prior distribution for a small diffusion time $T$ and starts the reverse diffusion process from it. ECM directly injects guidance gradients to the estimated clean manifold, eliminating extensive gradient backpropagation throughout the network. Our methodology streamlines the generative process, enabling practical applications with reduced computational overhead. Experimental validation on the large-scale Argoverse 2 dataset demonstrates our approach's superior performance, offering a viable solution for computationally efficient, high-quality joint trajectory generation and controllable generation for autonomous driving.
We propose optimizing the learnable Gaussian prior $\mathcal{N}(\mu,\Sigma)$ to enhance diffusion model performance with smaller steps, improving computational efficiency for diffusion-based autonomous driving applications.
We reformulate controllable generation as multi-objective optimization, propose Estimated Clean Manifold (ECM) Guidance to avoid gradient propagation, and use reference points to improve guided sampling efficiency, realism and effectiveness.
@inproceedings{wang2025optimizing,
title={Optimizing diffusion models for joint trajectory prediction and controllable generation},
author={Wang, Yixiao and Tang, Chen and Sun, Lingfeng and Rossi, Simone and Xie, Yichen and Peng, Chensheng and Hannagan, Thomas and Sabatini, Stefano and Poerio, Nicola and Tomizuka, Masayoshi and others},
booktitle={European Conference on Computer Vision},
pages={324--341},
year={2025},
organization={Springer}
}