Constrained Gaussian Processes With Integrated Kernels for Long-Horizon Prediction of Dense Pedestrian Crowd Flows

In this letter, we present a novel approach for predicting pedestrian crowd dynamics over longer time horizons (30 s). In dense environments over long time horizons, the number of pedestrian interactions is high, leading to the degradation of traditional pedestrian trajectory estimation techniques....

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Veröffentlicht in:	IEEE robotics and automation letters 2022-07, Vol.7 (3), p.7343-7350
Hauptverfasser:	Kiss, Stefan H., Katuwandeniya, Kavindie, Alempijevic, Alen, Vidal-Calleja, Teresa
Format:	Artikel
Sprache:	eng
Schlagworte:	Constraints Density Gaussian process Gaussian processes Microscopy Multi-modal perception for HRI Pedestrians Predictive models probabilistic inference Probabilistic logic Recurrent neural networks Robots Trajectory Trajectory analysis Uncertainty
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