{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:31:38Z","timestamp":1767965498072,"version":"3.49.0"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004040","name":"Flanders AI and the MACCHINA Project from Katholieke Universiteit Leuven","doi-asserted-by":"publisher","award":["C14\/18\/065"],"award-info":[{"award-number":["C14\/18\/065"]}],"id":[{"id":"10.13039\/501100004040","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ERC Advanced Grant","award":["H2020-ERC-2017-ADG 788506"],"award-info":[{"award-number":["H2020-ERC-2017-ADG 788506"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3224144","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T18:23:47Z","timestamp":1669314227000},"page":"123809-123834","source":"Crossref","is-referenced-by-count":11,"title":["Talk2Car: Predicting Physical Trajectories for Natural Language Commands"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1348-983X","authenticated-orcid":false,"given":"Thierry","family":"Deruyttere","sequence":"first","affiliation":[{"name":"Department of Computer Science, KU Leuven, Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7711-7528","authenticated-orcid":false,"given":"Dusan","family":"Grujicic","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, KU Leuven, Leuven, Belgium"}]},{"given":"Matthew B.","family":"Blaschko","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, KU Leuven, Leuven, Belgium"}]},{"given":"Marie-Francine","family":"Moens","sequence":"additional","affiliation":[{"name":"Department of Computer Science, KU Leuven, Leuven, Belgium"}]}],"member":"263","reference":[{"key":"ref73","first-page":"2337","article-title":"Efficient training of BERT by progressively stacking","author":"gong","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref71","first-page":"1","article-title":"ImageNet large scale visual recognition challenge","volume":"abs 1409 575","author":"russakovsky","year":"2014","journal-title":"CoRR"},{"key":"ref70","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref76","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc 2nd Int Conf Learn Represent (ICLR)"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.splu-1.7"},{"key":"ref74","first-page":"1","article-title":"VL-BERT: Pre-training of generic visual-linguistic representations","author":"su","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66096-3_4"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TSSC.1968.300136"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00142"},{"key":"ref78","author":"freedman","year":"2007","journal-title":"Statistics (International Student Edition)"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.388"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66096-3_2"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01276"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00808"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.493"},{"key":"ref37","first-page":"1","article-title":"Compositional attention networks for machine reasoning","volume":"abs 1803 3067","author":"hudson","year":"2018","journal-title":"CoRR"},{"key":"ref36","article-title":"Visual grounding with transformers","author":"du","year":"2021","journal-title":"arXiv 2105 04281"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00180"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66096-3_3"},{"key":"ref60","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"liu","year":"2019","journal-title":"arXiv 1907 11692"},{"key":"ref62","article-title":"Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving","author":"djuric","year":"2018","journal-title":"arXiv 1808 05819"},{"key":"ref61","article-title":"VOLO: Vision outlooker for visual recognition","author":"yuan","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1287"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_43"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref66","article-title":"Computing discrete Fréchet distance","author":"eiter","year":"1994"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00466"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74048-3_4"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2547641"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66096-3_1"},{"key":"ref2","author":"lambert","year":"2021","journal-title":"Tesla With Autopilot Engaged Approaches 10× Lower Chance of Accident Than Average Car Here's the Data"},{"key":"ref1","author":"schneider","year":"2017"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00272"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00315"},{"key":"ref21","article-title":"Objects as points","author":"zhou","year":"2019","journal-title":"arXiv 1904 07850"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00107"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00290"},{"key":"ref26","article-title":"Orthographic feature transform for monocular 3d object detection","author":"roddick","year":"2019","journal-title":"Proc Brit Mach Vis Conf"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00313"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00731"},{"key":"ref51","article-title":"RegFlow: Probabilistic flow-based regression for future prediction","author":"zi?ba","year":"2020","journal-title":"arXiv 2011 14620"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967929"},{"key":"ref58","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01495"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_45"},{"key":"ref54","first-page":"1","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref53","first-page":"1","article-title":"Goal-GAN: Multimodal trajectory prediction based on goal position estimation","author":"dendorfer","year":"2020","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref52","first-page":"1","article-title":"Neural ordinary differential equations","volume":"31","author":"chen","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1215"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01282"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8257985"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref13","first-page":"39","article-title":"Cosine meets softmax: A tough-to-beat baseline for visual grounding","author":"rufus","year":"2020","journal-title":"Computer Vision—ECCV 2020 Workshops"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19952"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01426"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16207"},{"key":"ref80","author":"krishna","year":"2019","journal-title":"EasyTurk A Wrapper for Custom AMT Tasks"},{"key":"ref4","author":"anderson","year":"2014","journal-title":"Autonomous vehicle technology a guide for policymakers"},{"key":"ref3","author":"litman","year":"2017","journal-title":"Autonomous Vehicle Implementation Predictions"},{"key":"ref6","author":"schoettle","year":"2014","journal-title":"A survey of public opinion about autonomous and self-driving vehicles in the US the UK and Australia"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-021-00041-8"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104257"},{"key":"ref7","author":"kim","year":"2021","journal-title":"Users' trust in and concerns about automated driving systems (research brief)"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00792"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01374-3"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01052"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01554"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3113781"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.conll-1.23"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1410"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.103"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09961196.pdf?arnumber=9961196","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T15:34:45Z","timestamp":1669995285000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9961196\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":80,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3224144","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}