{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:47:36Z","timestamp":1767340056822,"version":"3.28.0"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,23]]},"DOI":"10.1109\/icra46639.2022.9811585","type":"proceedings-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T19:36:40Z","timestamp":1657654600000},"page":"805-811","source":"Crossref","is-referenced-by-count":18,"title":["Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting"],"prefix":"10.1109","author":[{"given":"Rui","family":"Zhou","sequence":"first","affiliation":[{"name":"University of California,Department of Electrical Engineering and Computer Science,Berkeley,CA,USA,94720"}]},{"given":"Hongyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Michigan,Department of Aerospace Engineering,Ann Arbor,MI,USA,48109"}]},{"given":"Huidong","family":"Gao","sequence":"additional","affiliation":[{"name":"University of California,Department of Mechanical Engineering,Berkeley,CA,USA,94720"}]},{"given":"Masayoshi","family":"Tomizuka","sequence":"additional","affiliation":[{"name":"University of California,Department of Mechanical Engineering,Berkeley,CA,USA,94720"}]},{"given":"Jiachen","family":"Li","sequence":"additional","affiliation":[{"name":"Stanford University,Department of Aeronautics & Astronautics,CA,USA,94305"}]},{"given":"Zhuo","family":"Xu","sequence":"additional","affiliation":[{"name":"University of California,Department of Mechanical Engineering,Berkeley,CA,USA,94720"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015885"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2580401"},{"key":"ref32","first-page":"801","article-title":"Learning behavior patterns from video: A data-driven framework for agent-based crowd modeling","author":"zhong","year":"0","journal-title":"Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920917446"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00029"},{"key":"ref37","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2048101"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11015-4_18"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1167"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2016.7477732"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460504"},{"key":"ref14","first-page":"3483","article-title":"Learning structured output representation using deep conditional generative models","volume":"28","author":"sohn","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref15","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8813783"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3094821"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01579"},{"key":"ref27","first-page":"1025","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"0","journal-title":"Proceedings ofthe 31st International Conference on Neural Information Processing Systems"},{"key":"ref3","first-page":"19783","article-title":"Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning","volume":"33","author":"li","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3104334"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00031"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.51.4282"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2011.6130338"},{"key":"ref2","first-page":"683","article-title":"Tra-jectron++: Dynamically-feasible trajectory forecasting with heterogeneous data","author":"salzmann","year":"0","journal-title":"Computer Vision-ECCV 2020 16th European Conference Glasgow UK August 23-28 2020 Proceedings Part XVIII16"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2005.09.006"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref20","article-title":"Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks","author":"kosaraju","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01240"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref24","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"bai","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00246"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967822"},{"key":"ref26","article-title":"Graph attention networks","author":"velickovic","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref25","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"ArXiv Preprint"}],"event":{"name":"2022 IEEE International Conference on Robotics and Automation (ICRA)","start":{"date-parts":[[2022,5,23]]},"location":"Philadelphia, PA, USA","end":{"date-parts":[[2022,5,27]]}},"container-title":["2022 International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9811522\/9811357\/09811585.pdf?arnumber=9811585","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:03:41Z","timestamp":1667516621000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9811585\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/icra46639.2022.9811585","relation":{},"subject":[],"published":{"date-parts":[[2022,5,23]]}}}