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arXiv · 2106.04419

Asymmetrical Bi-RNN for pedestrian trajectory encoding

Raphaël Rozenberg·Joseph Gesnouin·Fabien Moutarde
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Abstract

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

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Raphaël Rozenberg, Joseph Gesnouin, Fabien Moutarde. 2021-06-01. Asymmetrical Bi-RNN for pedestrian trajectory encoding. https://arxiv.org/abs/2106.04419

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