Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising

TL;DR

This paper introduces the Energy Plan Denoising (EPD) model for stochastic pedestrian trajectory prediction, combining energy-based modeling and diffusion denoising to explicitly capture uncertainty and improve efficiency and accuracy.

Contribution

The novel EPD model integrates energy-based planning with diffusion denoising, modeling trajectory distributions directly and reducing iterative steps for efficient, uncertainty-aware pedestrian prediction.

Findings

Achieves state-of-the-art results on public datasets

Effectively models pedestrian intrinsic uncertainties

Reduces denoising iterations for efficiency

Abstract

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.