{"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/dynamic-scenario-representation-learning-for","title":"Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks","arxiv_id":"2303.04364","date":"2023-03-08","proceeding":null,"authors":["Xing Gao","Xiaogang Jia","Yikang Li","Hongkai Xiong"],"abstract":"Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a challenging problem in autonomous driving. Most existing works exploit static road graphs to characterize scenarios and are limited in modeling evolving spatio-temporal dependencies in dynamic scenarios. In this paper, we resort to dynamic heterogeneous graphs to model the scenario. Various scenario components including vehicles (agents) and lanes, multi-type interactions, and their changes over time are jointly encoded. Furthermore, we design a novel heterogeneous graph convolutional recurrent network, aggregating diverse interaction information and capturing their evolution, to learn to exploit intrinsic spatio-temporal dependencies in dynamic graphs and obtain effective representations of dynamic scenarios. Finally, with a motion forecasting decoder, our model predicts realistic and multi-modal future trajectories of agents and outperforms state-of-the-art published works on several motion forecasting benchmarks.","url_abs":"https://arxiv.org/abs/2303.04364v1","url_pdf":"https://arxiv.org/pdf/2303.04364v1.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":[],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"motion-forecasting","task_name":"Motion Forecasting"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/trajectory-prediction-on-argoverse","task":"Trajectory Prediction","dataset":"Argoverse","model":"HeteroGCN","rank_in_archive_order":1,"of":1,"metrics":{"MR (K=6)":"0.12","brier-minFDE (K=6)":"1.75","minADE (K=6)":"0.79","minFDE (K=6)":"1.16"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-argoverse2","task":"Trajectory Prediction","dataset":"Argoverse2","model":"HeteroGCN","rank_in_archive_order":1,"of":1,"metrics":{"MR (K=6)":"0.18","brier-minFDE (K=6)":"1.90","minADE (K=6)":"0.69","minFDE (K=6)":"1.34"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.04364","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}