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Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty
This paper introduces a novel approach for addressing a fundamental challenge in pedestrian trajectory prediction: reliable future state estimation in the presence of sensor noise and uncertain perception. It is common for traditional prediction models to produce overconfident and error-prone deterministic estimates. Previous research exploring probabilistic estimation methods often overlooked …