Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion

• URL: http://arxiv.org/abs/2303.08367v2
• Date: Sat, 11 May 2024 15:35:24 GMT
• Title: Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion
• Authors: Yao Liu, Zesheng Ye, Rui Wang, Binghao Li, Quan Z. Sheng, Lina Yao,
• Abstract summary: We present a model-agnostic uncertainty-aware pedestrian trajectory prediction framework. Unlike previous studies, we translate the predictiveity to explicit distributions, allowing it to generate plausible future trajectories. Our framework is compatible with different neural net architectures.
• Score: 26.715578412088327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tremendous efforts have been put forth on predicting pedestrian trajectory with generative models to accommodate uncertainty and multi-modality in human behaviors. An individual's inherent uncertainty, e.g., change of destination, can be masked by complex patterns resulting from the movements of interacting pedestrians. However, latent variable-based generative models often entangle such uncertainty with complexity, leading to limited either latent expressivity or predictive diversity. In this work, we propose to separately model these two factors by implicitly deriving a flexible latent representation to capture intricate pedestrian movements, while integrating predictive uncertainty of individuals with explicit bivariate Gaussian mixture densities over their future locations. More specifically, we present a model-agnostic uncertainty-aware pedestrian trajectory prediction framework, parameterizing sufficient statistics for the mixture of Gaussians that jointly comprise the multi-modal trajectories. We further estimate these parameters of interest by approximating a denoising process that progressively recovers pedestrian movements from noise. Unlike previous studies, we translate the predictive stochasticity to explicit distributions, allowing it to readily generate plausible future trajectories indicating individuals' self-uncertainty. Moreover, our framework is compatible with different neural net architectures. We empirically show the performance gains over state-of-the-art even with lighter backbones, across most scenes on two public benchmarks.

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