- The paper introduces Optimal Gaussian Diffusion (OGD) to reduce inference steps while maintaining high prediction accuracy.
- It presents ECM Guidance to streamline guided sampling by directly injecting gradients into the clean data manifold.
- Experimental results on the Argoverse 2 dataset confirm reduced computational costs and real-time applicability in autonomous driving.
Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation
The paper presents a methodological advancement in the application of diffusion models to autonomous driving, specifically targeting the tasks of joint trajectory prediction and controllable trajectory generation. These tasks are critical in autonomous driving, where accurate prediction of vehicle paths and the ability to control these paths under specific constraints are essential for safe navigation in dynamic environments.
The paper introduces two novel techniques: Optimal Gaussian Diffusion (OGD) and Estimated Clean Manifold (ECM) Guidance, which effectively mitigate the challenges of high computational cost and inefficiency typically associated with diffusion models.
Optimal Gaussian Diffusion (OGD)
OGD is a technique designed to accelerate the reverse diffusion process in diffusion models. Traditional diffusion models start the diffusion process from a non-informative prior, often a standard Gaussian distribution. This approach generally necessitates a large number of diffusion steps to achieve high-quality generation, which can be computationally expensive. OGD, however, incorporates an informative prior that approximates the intermediate distribution of data at a specific noise level. Through analytical formulations, OGD determines the optimal Gaussian prior and a perturbation kernel. This prior minimizes the divergence from the target data distribution, thereby reducing the required diffusion length at inference time.
Experimental data from the Argoverse 2 dataset highlight that OGD can perform comparably or even outperform conventional diffusion models with significantly fewer diffusion steps. For instance, OGD achieves competitive prediction metrics with only a fraction of the steps required by standard diffusion approaches.
Estimated Clean Manifold (ECM) Guidance
ECM Guidance addresses inefficiencies in guided sampling, a crucial aspect of controllable generation in diffusion models. Typically, guided sampling involves backpropagating through the entire network to incorporate guidance gradients, an approach that is computationally intensive. ECM Guidance simplifies this process by injecting gradients directly into the clean data manifold. It treats the guided sampling as a multi-objective optimization problem, focusing on reducing both the likelihood error and guidance cost, thus achieving control with reduced computational demands.
The authors further extend ECM with Estimated Clean Manifold with Reference Joint Trajectory (ECMR), which leverages reference trajectories derived from marginal predictions to stabilize and accelerate the optimization process, addressing challenges related to multi-modal distribution sampling.
Experimental Results and Implications
The experimental results validate that OGD and ECMR can produce highly accurate joint trajectory predictions and controllable generation outputs with significantly reduced computational demands. The OGD framework particularly excels in scenarios requiring fast inference under limited computational resources, making it well-suited for real-time applications in autonomous vehicle systems.
The paper elucidates the potential of diffusion models in addressing complex, real-world problems in autonomous driving through enhanced computational efficiency and precision. The OGD and ECM techniques lay groundwork for further explorations into the application of diffusion models across dynamic, high-stakes environments, and offer insights that could steer future research into more efficient and versatile AI systems for real-time applications.