{"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/trajectory-forecasting-on-temporal-graphs","title":"Trajectory Forecasting on Temporal Graphs","arxiv_id":"2207.00255","date":"2022-07-01","proceeding":null,"authors":["Görkay Aydemir","Adil Kaan Akan","Fatma Güney"],"abstract":"Predicting future locations of agents in the scene is an important problem in self-driving. In recent years, there has been a significant progress in representing the scene and the agents in it. The interactions of agents with the scene and with each other are typically modeled with a Graph Neural Network. However, the graph structure is mostly static and fails to represent the temporal changes in highly dynamic scenes. In this work, we propose a temporal graph representation to better capture the dynamics in traffic scenes. We complement our representation with two types of memory modules; one focusing on the agent of interest and the other on the entire scene. This allows us to learn temporally-aware representations that can achieve good results even with simple regression of multiple futures. When combined with goal-conditioned prediction, we show better results that can reach the state-of-the-art performance on the Argoverse benchmark.","url_abs":"https://arxiv.org/abs/2207.00255v1","url_pdf":"https://arxiv.org/pdf/2207.00255v1.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":"trajectory-forecasting-on-temporal-graphs","repo_url":"https://github.com/gorkaydemir/FTGN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"motion-forecasting","task_name":"Motion Forecasting"},{"task_slug":"trajectory-forecasting","task_name":"Trajectory Forecasting"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/motion-forecasting-on-argoverse-cvpr-2020","task":"Motion Forecasting","dataset":"Argoverse CVPR 2020","model":"FTGN","rank_in_archive_order":51,"of":299,"metrics":{"DAC (K=6)":"0.9837","MR (K=1)":"0.5984","MR (K=6)":"0.1528","brier-minFDE (K=6)":"1.9285","minADE (K=1)":"1.7716","minADE (K=6)":"0.8607","minFDE (K=1)":"3.9031","minFDE (K=6)":"1.3055"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.00255","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}