Papers › Asymmetrical Bi-RNN for pedestrian trajectory encoding
Asymmetrical Bi-RNN for pedestrian trajectory encoding
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: github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Trajectory Forecasting | TrajNet++ | U-LSTM + Social Pooling | COL | 6.560 | #2 of 3 | Archive leaderboard | report |
| Trajectory Forecasting | TrajNet++ | U-LSTM + Social Pooling | FDE | 1.150 | #2 of 3 | Archive leaderboard | report |
| Trajectory Prediction | TrajNet++ | U-LSTM + Social Pooling | COL | 6.560 | #2 of 3 | Archive leaderboard | report |
| Trajectory Prediction | TrajNet++ | U-LSTM + Social Pooling | FDE | 1.150 | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: U-RNNs
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