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@INPROCEEDINGS{Alia:1044146,
      author       = {Alia, Ahmed and Chraibi, Mohcine and Seyfried, Armin},
      title        = {{A} {D}ynamic {D}istance {S}ocial {LSTM} for {P}redicting
                      {P}edestrian {T}rajectories in {C}rowded {E}nvironments},
      reportid     = {FZJ-2025-03048},
      year         = {2025},
      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.},
      month         = {Jul},
      date          = {2025-07-07},
      organization  = {Modelling, Data Analytics and AI in
                       Engineering, Porto (Portugal), 7 Jul
                       2025 - 11 Jul 2025},
      subtyp        = {After Call},
      cin          = {IAS-7},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.34734/FZJ-2025-03048},
      url          = {https://juser.fz-juelich.de/record/1044146},
}