  Pedestrian motion behavior involves a combination of individual goals and
social interactions with other agents. In this article, we present an
asymmetrical bidirectional recurrent neural network architecture called U-RNN
to encode pedestrian trajectories and evaluate its relevance to replace LSTMs
for various forecasting models. Experimental results on the Trajnet++ benchmark
show that the U-LSTM variant yields better results regarding every available
metrics (ADE, FDE, Collision rate) than common trajectory encoders for a
variety of approaches and interaction modules, suggesting that the proposed
approach is a viable alternative to the de facto sequence encoding RNNs.
  Our implementation of the asymmetrical Bi-RNNs for the Trajnet++ benchmark is
available at:
github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories
