Asymmetrical Bi-RNN for Pedestrian Trajectory Encoding

Raphaël Rozenberg (1, 2) , Fabien Moutarde (1) , Joseph Gesnouin (3, 1)

1 CAOR - Centre de Robotique
2 ENS-PSL - École normale supérieure - Paris
3 VeDeCom - VEhicule DEcarboné et COmmuniquant et sa Mobilité

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-toencode-pedestrian-trajectories.

Mots clés: Sequence Encoding; Pedestrian Safety; Trajectory Forecasting

Domaines: Intelligence artificielle [cs.AI]

Raphaël Rozenberg, Fabien Moutarde, Joseph Gesnouin. Asymmetrical Bi-RNN for Pedestrian Trajectory Encoding. Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP), Jul 2022, Vannes, France. ⟨hal-03682456⟩