{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:56:15Z","timestamp":1793116575271,"version":"4.5.0"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"PIA French project CAMPUS"},{"name":"Connected Automated Mobility Platform for Urban Sustainability"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Veh."],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1109\/tiv.2020.2991952","type":"journal-article","created":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T16:09:48Z","timestamp":1588349388000},"page":"175-185","source":"Crossref","is-referenced-by-count":421,"title":["Attention Based Vehicle Trajectory Prediction"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4602-8100","authenticated-orcid":false,"given":"Kaouther","family":"Messaoud","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itheri","family":"Yahiaoui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3365-5463","authenticated-orcid":false,"given":"Anne","family":"Verroust-Blondet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4209-1233","authenticated-orcid":false,"given":"Fawzi","family":"Nashashibi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569552"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2942089"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500658"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995919"},{"key":"ref37","first-page":"4967","article-title":"A simple neural network module for relational reasoning","author":"santoro","year":"0","journal-title":"Proc Adv Neural Inf Process Syst 30"},{"key":"ref36","article-title":"US Highway I-80 dataset","author":"colyar","year":"2006","journal-title":"Federal Highway Administration (FHWA)"},{"key":"ref35","article-title":"US Highway 101 dataset","author":"colyar","year":"2007","journal-title":"Federal Highway Administration (FHWA)"},{"key":"ref34","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"0","journal-title":"Proc Adv Neural Inf Process Syst 30"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2008.4621210"},{"key":"ref40","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc 3rd Int Conf Learn Representations"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2008.2011691"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.2514\/6.2011-1512"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2014.6856480"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2009.5164354"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2014.6856491"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2013.6696982"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2010.5509919"},{"key":"ref18","first-page":"149","article-title":"High-speed highway scene prediction based on driver models learned from demonstrations","author":"sierra gonz\u00e1lez","year":"0","journal-title":"Proc IEEE Int Conf Intell Transp Syst"},{"key":"ref19","first-page":"1","article-title":"Interaction-aware driver maneuver inference in highways using realistic driver models","author":"sierra gonz\u00e1lez","year":"0","journal-title":"Proc IEEE Int Conf Intell Transp Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995948"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01240"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995797"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500493"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317913"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8916887"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00196"},{"key":"ref2","article-title":"Human motion trajectory prediction: A survey","author":"rudenko","year":"2019","journal-title":"arXiv 1905 06113"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2006.1707409"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8813829"},{"key":"ref20","first-page":"3812","article-title":"Infogail: Interpretable imitation learning from visual demonstrations","author":"li","year":"0","journal-title":"Proc Adv Neural Inf Process Syst 30"},{"key":"ref22","article-title":"Deep imitative models for flexible inference, planning, and control","author":"rhinehart","year":"0","journal-title":"arXiv 1810 06544"},{"key":"ref21","first-page":"4565","article-title":"Generative adversarial imitation learning","author":"ho","year":"0","journal-title":"Proc Adv Neural Inf Process Syst 29"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2017.09.018"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569595"},{"key":"ref41","article-title":"Automatic differentiation in pytorch","author":"paszke","year":"0","journal-title":"Proc NIPS Autodiff Workshop Future Gradient-Based Mach Learn Softw Techn"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794146"},{"key":"ref26","article-title":"Scene compliant trajectory forecast with agent-centric spatio-temporal grids","author":"ridel","year":"2019","journal-title":"arXiv 1909 07507"},{"key":"ref25","article-title":"Multimodal trajectory predictions for autonomous driving using deep convolutional networks","author":"cui","year":"2018","journal-title":"arXiv 1809 10732"}],"container-title":["IEEE Transactions on Intelligent Vehicles"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7274857\/9361468\/09084255.pdf?arnumber=9084255","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T10:54:18Z","timestamp":1652180058000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9084255\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":42,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tiv.2020.2991952","relation":{},"ISSN":["2379-8904","2379-8858"],"issn-type":[{"value":"2379-8904","type":"electronic"},{"value":"2379-8858","type":"print"}],"subject":[],"published":{"date-parts":[[2021,3]]}}}