{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/asymmetrical-bi-rnn-for-pedestrian-trajectory","title":"Asymmetrical Bi-RNN for pedestrian trajectory encoding","arxiv_id":"2106.04419","date":"2021-06-01","proceeding":null,"authors":["Raphaël Rozenberg","Joseph Gesnouin","Fabien Moutarde"],"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","url_abs":"https://arxiv.org/abs/2106.04419v2","url_pdf":"https://arxiv.org/pdf/2106.04419v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"asymmetrical-bi-rnn-for-pedestrian-trajectory","repo_url":"https://github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-navigation","task_name":"Autonomous Navigation"},{"task_slug":"pedestrian-trajectory-prediction","task_name":"Pedestrian Trajectory Prediction"},{"task_slug":"trajectory-forecasting","task_name":"Trajectory Forecasting"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[{"method_slug":"u-rnns","method_name":"U-RNNs"}],"datasets_introduced":[],"methods_introduced":[{"slug":"u-rnns","name":"U-RNNs","full_name":"Asymmetrical Bi-RNN"}],"results":[{"leaderboard":"/sota/trajectory-forecasting-on-trajnet","task":"Trajectory Forecasting","dataset":"TrajNet++","model":"U-LSTM + Social Pooling","rank_in_archive_order":2,"of":3,"metrics":{"COL":"6.560","FDE":"1.150"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-trajnet","task":"Trajectory Prediction","dataset":"TrajNet++","model":"U-LSTM + Social Pooling","rank_in_archive_order":2,"of":3,"metrics":{"COL":"6.560","FDE":"1.150"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}