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| Hauptseite > Publikationsdatenbank > A Dynamic Distance Social LSTM for Predicting Pedestrian Trajectories in Crowded Environments |
| Hauptseite > Publikationsdatenbank > A Dynamic Distance Social LSTM for Predicting Pedestrian Trajectories in Crowded Environments |
| Conference Presentation (After Call) | FZJ-2025-03048 |
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2025
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Please use a persistent id in citations: doi:10.34734/FZJ-2025-03048
Abstract: This work introduces dynamic distance Social Long Short-Term Memory, a deep learning approach for pedestrian trajectory prediction in crowded environments. The approach integrates a new dynamic distance-based loss function into Social Long Short-Term Memory, enhancing collision avoidance without compromising displacement accuracy. The method is trained and evaluated on a heterogeneous density dataset and four homogeneous density datasets, covering various crowd-density levels. Experimental results show that the proposed approach outperforms baseline methods in reducing collision rates without decreasing displacement accuracy and, in most cases, even improving it.
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