{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:29:01Z","timestamp":1775744941287,"version":"3.50.1"},"reference-count":69,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"15","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2022YFB4300400"],"award-info":[{"award-number":["2022YFB4300400"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52441202"],"award-info":[{"award-number":["52441202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52202378"],"award-info":[{"award-number":["52202378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Transport of PRC Key Laboratory of Transport Industry of Comprehensive Transportation Theory","award":["MTF2023002"],"award-info":[{"award-number":["MTF2023002"]}]},{"name":"National Key Research and Development Project of China","award":["2024YFB43000303"],"award-info":[{"award-number":["2024YFB43000303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025,8,1]]},"DOI":"10.1109\/jiot.2025.3571492","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T13:58:30Z","timestamp":1747663110000},"page":"30820-30834","source":"Crossref","is-referenced-by-count":3,"title":["Brimory: Bringing Humanoid Memory Into Trajectory Prediction Model for Autonomous Driving"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7155-6178","authenticated-orcid":false,"given":"Zhengxing","family":"Lan","sequence":"first","affiliation":[{"name":"School of Transportation Science and Engineering, and the State Key Laboratory of Intelligent Transportation System, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7009-7499","authenticated-orcid":false,"given":"Lingshan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, and the State Key Laboratory of Intelligent Transportation System, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3504-8963","authenticated-orcid":false,"given":"Yilong","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering and the State Key Laboratory of Intelligent Transportation System, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5780-4312","authenticated-orcid":false,"given":"Zhiyong","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, and the State Key Laboratory of Intelligent Transportation System, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0704-7077","authenticated-orcid":false,"given":"Haiyang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering and the State Key Laboratory of Intelligent Transportation System, Beihang University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3506720"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA57147.2024.10610610"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/tiv.2024.3418522"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3376074"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2024.3360946"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3357479"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00706"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2024.104735","article-title":"Hi-SCL: Fighting long-tailed challenges in trajectory prediction with hierarchical wave-semantic contrastive learning","volume":"165","author":"Lan","year":"2024","journal-title":"Transp. Res. C, Emerg. Technol."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-023-01799-z"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3389\/fnbeh.2013.00027"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2021.08.002"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2017.00438"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40903-9"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01404"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27763"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2940992"},{"key":"ref17","first-page":"203","article-title":"Multimodal trajectory prediction conditioned on lane-graph traversals","volume-title":"Proc. Conf. Robot Learn.","author":"Deo"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC55140.2022.9921850"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-54069-5"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/285"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00376"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/itsc.2018.8569595"},{"key":"ref23","first-page":"947","article-title":"IntentNet: Learning to predict intention from raw sensor data","volume-title":"Proc. Conf. Robot Learn.","author":"Casas"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00749"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812253"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3287186"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3331143"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109592"},{"key":"ref29","article-title":"SocialFormer: Social interaction modeling with edge-enhanced heterogeneous graph transformers for trajectory prediction","author":"Wang","year":"2024","journal-title":"arXiv:2405.03809"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2023.10.001"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812100"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00770"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9811637"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2024.103748"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00226"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00967"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110990"},{"key":"ref38","article-title":"DICE: Diverse diffusion model with scoring for trajectory prediction","author":"Choi","year":"2023","journal-title":"arXiv:2310.14570"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109102"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3273572"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2019.00040"},{"key":"ref42","article-title":"Brain-inspired deep imitation learning for autonomous driving systems","author":"Ahmedov","year":"2021","journal-title":"arXiv:2107.14654"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00237-3"},{"key":"ref44","article-title":"LAFormer: Trajectory prediction for autonomous driving with lane-aware scene constraints","author":"Liu","year":"2023","journal-title":"arXiv:2302.13933"},{"key":"ref45","first-page":"1","article-title":"FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Yi"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01230"},{"key":"ref47","first-page":"1","article-title":"Frequency-domain MLPs are more effective learners in time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Yi"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i16.29803"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00862"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3295990"},{"key":"ref53","article-title":"Latent variable sequential set transformers for joint multi-agent motion prediction","author":"Girgis","year":"2021","journal-title":"arXiv:2104.00563"},{"key":"ref54","article-title":"Thomas: Trajectory heatmap output with learned multi-agent sampling","author":"Gilles","year":"2021","journal-title":"arXiv:2110.06607"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20047-2_8"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72658-3_21"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC55140.2022.9922432"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_32"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3342430"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01116"},{"key":"ref61","first-page":"1793","article-title":"SSL-Lanes: Selfsupervised learning for motion forecasting in autonomous driving","volume-title":"Proc. Conf. Robot Learn.","author":"Bhattacharyya"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636035"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01662"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02106"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160468"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3375900"},{"key":"ref67","article-title":"Trajectory forecasting on temporal graphs","author":"Aydemir","year":"2022","journal-title":"arXiv:2207.00255"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3329885"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11096042\/11007114.pdf?arnumber=11007114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T21:35:10Z","timestamp":1764711310000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11007114\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"references-count":69,"journal-issue":{"issue":"15"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3571492","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,1]]}}}