Asymmetrical Bi-RNN for pedestrian trajectory encoding

1 Jun 2021  ·  Raphaël Rozenberg, Joseph Gesnouin, Fabien Moutarde ·

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:

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Trajectory Prediction TrajNet++ U-LSTM + Social Pooling FDE 1.150 # 2
COL 6.560 # 2
Trajectory Forecasting TrajNet++ U-LSTM + Social Pooling FDE 1.150 # 2
COL 6.560 # 2