{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:16:46Z","timestamp":1781018206918,"version":"3.54.1"},"reference-count":44,"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\/icra46639.2022.9812253","type":"proceedings-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T19:36:40Z","timestamp":1657654600000},"page":"9107-9114","source":"Crossref","is-referenced-by-count":209,"title":["GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation"],"prefix":"10.1109","author":[{"given":"Thomas","family":"Gilles","sequence":"first","affiliation":[{"name":"Paris Research Center, Huawei Technologies,IoV team,France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Sabatini","sequence":"additional","affiliation":[{"name":"Paris Research Center, Huawei Technologies,IoV team,France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dzmitry","family":"Tsishkou","sequence":"additional","affiliation":[{"name":"Paris Research Center, Huawei Technologies,IoV team,France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bogdan","family":"Stanciulescu","sequence":"additional","affiliation":[{"name":"MINES ParisTech, PSL University, Center for robotics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabien","family":"Moutarde","sequence":"additional","affiliation":[{"name":"MINES ParisTech, PSL University, Center for robotics"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","author":"mo","year":"2020","journal-title":"ReCoG A deep learning framework with heterogeneous graph for interaction-aware trajectory prediction"},{"key":"ref38","author":"mo","year":"2021","journal-title":"Heterogeneous edge-enhanced graph attention network for multi-agent trajectory prediction"},{"key":"ref33","author":"wang","year":"2021","journal-title":"Step-wise goal-driven networks for trajectory prediction"},{"key":"ref32","article-title":"Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data","author":"salzmann","year":"0","journal-title":"ECCV"},{"key":"ref31","year":"0","journal-title":"Nuscenes prediction competition"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"ref37","author":"deo","year":"2020","journal-title":"Trajectory forecasts in unknown environments conditioned on grid-based plans"},{"key":"ref36","author":"luo","year":"2020","journal-title":"Probabilistic multimodal trajectory prediction with lane attention for autonomous vehicles"},{"key":"ref35","author":"messaoud","year":"2020","journal-title":"Multi-Head Attention with Joint Agent-Map Representation for Trajectory Prediction in Autonomous Driving"},{"key":"ref34","author":"khandelwal","year":"2020","journal-title":"What-If Motion Prediction for Autonomous Driving"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9564944"},{"key":"ref40","author":"scibior","year":"2021","journal-title":"Imagining the road ahead Multi-agent trajectory prediction via differentiable simulation"},{"key":"ref11","author":"mangalam","year":"2020","journal-title":"From Goals Waypoints & Paths To Long Term Human Trajectory Forecasting"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317913"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref14","article-title":"Multiple futures prediction","author":"tang","year":"0","journal-title":"NeurIPS"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00749"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"ref17","article-title":"Learning lane graph representations for motion forecasting","author":"liang","year":"0","journal-title":"ECCV"},{"key":"ref18","author":"song","year":"2021","journal-title":"Learning to Predict Vehicle Trajectories with Model-based Planning"},{"key":"ref19","article-title":"Map-adaptive goal-based trajectory prediction","author":"zhang","year":"0","journal-title":"CoRL"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01116"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01408"},{"key":"ref27","year":"0","journal-title":"Argoverse Motion Forecasting Competition"},{"key":"ref3","article-title":"Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction","author":"chai","year":"0","journal-title":"CoRL"},{"key":"ref6","article-title":"Tnt: Target-driven trajectory prediction","author":"zhao","year":"0","journal-title":"CoRL"},{"key":"ref29","author":"ngiam","year":"2021","journal-title":"Scene transformer A unified multi-task model for behavior prediction and planning"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793868"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01554"},{"key":"ref7","author":"zeng","year":"2021","journal-title":"LaneRCNN Distributed Representations for Graph-Centric Motion Forecasting"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9197340"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01440"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500493"},{"key":"ref20","article-title":"R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting","author":"rhinehart","year":"0","journal-title":"ECCV"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref21","article-title":"It is not the journey but the destination: Endpoint conditioned trajectory prediction","author":"mangalam","year":"0","journal-title":"ECCV"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref24","author":"erdem","year":"0","journal-title":"6th Place Solution Very Custom GRU"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref44","author":"ettinger","year":"2021","journal-title":"Large scale interactive motion forecasting for autonomous driving The waymo open motion dataset"},{"key":"ref26","author":"zhou","year":"2019","journal-title":"Objects as points"},{"key":"ref43","author":"ba","year":"2016","journal-title":"Layer normalization"},{"key":"ref25","author":"rozenberg","year":"2021","journal-title":"Asymmetrical bi-rnn for pedestrian trajectory encoding"}],"event":{"name":"2022 IEEE International Conference on Robotics and Automation (ICRA)","location":"Philadelphia, PA, USA","start":{"date-parts":[[2022,5,23]]},"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\/09812253.pdf?arnumber=9812253","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:07:10Z","timestamp":1667516830000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9812253\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/icra46639.2022.9812253","relation":{},"subject":[],"published":{"date-parts":[[2022,5,23]]}}}