Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving, but they face challenges of inefficient inference steps 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 prediction and controllable generation for autonomous driving. Our project webpage is at https://yixiaowang7.github.io/OptTrajDiff_Page/.

本研究针对扩散模型在自动驾驶中的联合轨迹预测和可控生成面临的低效推理步骤和高计算需求的问题，提出了最佳高斯扩散（OGD）和估计清晰流形（ECM）引导的新方法。研究结果表明，该方法降低了计算负担，提供了高质量的联合轨迹预测和可控生成的有效解决方案。

联合轨迹预测和可控生成的扩散模型优化