  Pedestrian trajectory prediction plays a pivotal role in the realms of
autonomous driving and smart cities. Despite extensive prior research employing
sequence and generative models, the unpredictable nature of pedestrians,
influenced by their social interactions and individual preferences, presents
challenges marked by uncertainty and multimodality. In response, we propose the
Energy Plan Denoising (EPD) model for stochastic trajectory prediction. EPD
initially provides a coarse estimation of the distribution of future
trajectories, termed the Plan, utilizing the Langevin Energy Model.
Subsequently, it refines this estimation through denoising via the
Probabilistic Diffusion Model. By initiating denoising with the Plan, EPD
effectively reduces the need for iterative steps, thereby enhancing efficiency.
Furthermore, EPD differs from conventional approaches by modeling the
distribution of trajectories instead of individual trajectories. This allows
for the explicit modeling of pedestrian intrinsic uncertainties and eliminates
the need for multiple denoising operations. A single denoising operation
produces a distribution from which multiple samples can be drawn, significantly
enhancing efficiency. Moreover, EPD's fine-tuning of the Plan contributes to
improved model performance. We validate EPD on two publicly available datasets,
where it achieves state-of-the-art results. Additionally, ablation experiments
underscore the contributions of individual modules, affirming the efficacy of
the proposed approach.
