Pedestrian trajectory prediction using BiRNN encoder–decoder framework*

Jiaxu Wu, Hanwool Woo, Yusuke Tamura, Alessandro Moro, Stefano Massaroli, Atsushi Yamashita, Hajime Asama

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Autonomous mobile robots navigating through human crowds are required to foresee the future trajectories of surrounding pedestrians and accordingly plan safe paths to avoid any possible collision. This paper presents a novel approach for pedestrian trajectory prediction. In particular, we developed a new method based on an encoder–decoder framework using bidirectional recurrent neural networks (BiRNN). The difficulty of incorporating social interactions into the model has been addressed thanks to the special structure of BiRNN enhanced by the attention mechanism, a proximity-independent model of the relative importance of each pedestrian. The main difference between our and the previous approaches is that BiRNN allows us to employs information on the future state of the pedestrians. We tested the performance of our method on several public datasets. The proposed model outperforms the current state-of-the-art approaches on most of these datasets. Furthermore, we analyze the resulting predicted trajectories and the learned attention scores to prove the advantages of BiRRNs on recognizing social interactions.

Original languageEnglish
Pages (from-to)956-969
Number of pages14
JournalAdvanced Robotics
Issue number18
Publication statusPublished - 2019 Sept 17
Externally publishedYes


  • Pedestrian trajectory prediction
  • artificial neural network (ANN)
  • encoder–decoder framework

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications


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