Pedestrian motion behavior involves a combination of individual goals and
social interactions with other agents. In this article, we present a
non-symmetrical bidirectional recurrent neural network architecture called
U-RNN as a sequence encoder and evaluate its relevance to replace LSTMs for
various forecasting models. Experimental results on the Trajnet++ benchmark
show that the U-LSTM variant can yield better results regarding every available
metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a
variety of approaches and interaction modules.
Our implementation of the asymmetrical Bi-RNNs for the Trajnet++ benchmark is
available at:
github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectoriesComment: 7 page