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| Hauptseite > Publikationsdatenbank > A Dynamic Distance Social LSTM for Predicting Pedestrian Trajectories in Crowded Environments > print |
| Hauptseite > Publikationsdatenbank > A Dynamic Distance Social LSTM for Predicting Pedestrian Trajectories in Crowded Environments > print |
| 001 | 1044146 | ||
| 005 | 20250729202320.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2025-03048 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-03048 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Alia, Ahmed |0 P:(DE-Juel1)185971 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Modelling, Data Analytics and AI in Engineering |g MadeAI 2025 |c Porto |d 2025-07-07 - 2025-07-11 |w Portugal |
| 245 | _ | _ | |a A Dynamic Distance Social LSTM for Predicting Pedestrian Trajectories in Crowded Environments |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a 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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| 700 | 1 | _ | |a Chraibi, Mohcine |0 P:(DE-Juel1)132077 |b 1 |u fzj |
| 700 | 1 | _ | |a Seyfried, Armin |0 P:(DE-Juel1)132266 |b 2 |u fzj |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1044146/files/Abstract.pdf |
| 856 | 4 | _ | |y Restricted |u https://juser.fz-juelich.de/record/1044146/files/MADEAI25-Final.pptx |
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| 914 | 1 | _ | |y 2025 |
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