Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion
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Tremendous efforts have been devoted to pedestrian trajectory prediction using generative modeling for accommodating 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 either limited expressivity or overconfident predictions. In this work, we propose to separately model these two factors by implicitly deriving a flexible distribution that describes complex pedestrians' movements, whereas incorporating predictive uncertainty of individuals with explicit density functions over their future locations. More specifically, we present an uncertainty-aware pedestrian trajectory prediction framework, parameterizing sufficient statistics for the distributions of locations 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 prior studies, we translate the predictive stochasticity to the explicit distribution, making it readily used to generate plausible future trajectories indicating individuals' self-uncertainty. Moreover, our framework is model-agnostic for compatibility with different neural network architectures. We empirically show the performance advantages of our framework on widely-used benchmarks, outperforming state-of-the-art in most scenes even with lighter backbones.
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