{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:06:32Z","timestamp":1778537192400,"version":"3.51.4"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Shanghai Municipal Science and Technology Major","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]},{"name":"ZJ Lab"},{"name":"Shanghai Center"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Veh."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tiv.2023.3299600","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T17:36:42Z","timestamp":1690565802000},"page":"1780-1791","source":"Crossref","is-referenced-by-count":12,"title":["Bridging the Gap: Improving Domain Generalization in Trajectory Prediction"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5292-9047","authenticated-orcid":false,"given":"Zhibo","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7015-6437","authenticated-orcid":false,"given":"Jiayu","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3022-7340","authenticated-orcid":false,"given":"Haiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Mogo Auto Intelligence and Telematics Information Technology Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8151-0059","authenticated-orcid":false,"given":"Ru","family":"Wan","sequence":"additional","affiliation":[{"name":"Mogo Auto Intelligence and Telematics Information Technology Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5924-3360","authenticated-orcid":false,"given":"Junping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0892-1213","authenticated-orcid":false,"given":"Jian","family":"Pu","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2021.3065867"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2020.3044180"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2021.3061907"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2019.2919477"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2017.2730588"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3150668"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2019.2904390"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2017.2769882"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2020.2991952"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812070"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3167103"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3243004"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4241523"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01661"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01292"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00553"},{"key":"ref17","first-page":"1","article-title":"Towards understanding ensemble, knowledge distillation and self-distillation in deep learning","volume-title":"Proc. Int. Conf. Learn. Representation","author":"Allen-Zhu","year":"2023"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/OJITS.2023.3233952"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref20","first-page":"1","article-title":"Multiple futures prediction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tang","year":"2019"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"ref22","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Simonyan","year":"2015"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00865"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3161453"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3267902"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_32"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9564944"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812253"},{"key":"ref32","first-page":"1","article-title":"THOMAS: Trajectory heatmap output with learned multi-agent sampling","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gilles","year":"2022"},{"key":"ref33","first-page":"895","article-title":"TNT: Target-driven trajectory prediction","volume-title":"Proc. Conf. Robot Learn.","author":"Zhao","year":"2020"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00862"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref39","first-page":"1","article-title":"Scene transformer: A unified architecture for predicting future trajectories of multiple agents","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ngiam","year":"2022"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539360"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-443-18424-6.00001-5"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01005"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812368"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00644"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00381"},{"key":"ref47","first-page":"6415","article-title":"Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps","volume-title":"Proc. IEEE\/RSJ Int. Conf. Intell. Robots Syst.","author":"Zhan","year":"2019"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref49","first-page":"1","article-title":"SGDR: Stochastic gradient descent with warm restarts","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Loshchilov","year":"2017"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812060"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3256982"}],"container-title":["IEEE Transactions on Intelligent Vehicles"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7274857\/10443740\/10197222.pdf?arnumber=10197222","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T17:40:00Z","timestamp":1751305200000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10197222\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":51,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tiv.2023.3299600","relation":{},"ISSN":["2379-8904","2379-8858"],"issn-type":[{"value":"2379-8904","type":"electronic"},{"value":"2379-8858","type":"print"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}