{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:10:10Z","timestamp":1775027410720,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62173078"],"award-info":[{"award-number":["62173078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Natural Science Foundation of Liaoning Province","award":["2022-MS-268"],"award-info":[{"award-number":["2022-MS-268"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07304-9","type":"journal-article","created":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T12:00:26Z","timestamp":1746273626000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MGFormer: a multi-information-based GRU-transformer network for pedestrian trajectory prediction"],"prefix":"10.1007","volume":"81","author":[{"given":"Quankai","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Sang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wangxing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,3]]},"reference":[{"key":"7304_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112675","volume":"213","author":"HF Sang","year":"2023","unstructured":"Sang HF, Chen WX, Wang JY, Zhao ZS (2023) Rdgcn: Reasonably dense graph convolution network for pedestrian trajectory prediction. Measurement 213:112675","journal-title":"Measurement"},{"key":"7304_CR2","doi-asserted-by":"crossref","unstructured":"R\u00f6smann C, Oeljeklaus M, Hoffmann F (2017) Online trajectory prediction and planing for social robot navigation. In: 2017 IEEE international conference on advanced intelligent mechatronics (AIM)","DOI":"10.1109\/AIM.2017.8014190"},{"key":"7304_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103971","volume":"103","author":"JX Yao","year":"2020","unstructured":"Yao JX, Ye YT (2020) The effect of image recognition traffic prediction method under deep learning and Naive Bayes algorithm on freeway traffic safety. Image Vis Comput 103:103971","journal-title":"Image Vis Comput"},{"issue":"5","key":"7304_CR4","doi-asserted-by":"publisher","first-page":"3296","DOI":"10.3390\/app13053296","volume":"13","author":"D Liu","year":"2023","unstructured":"Liu D, Li Q, Li S, Kong J, Qi M (2023) Non-autoregressive sparse transformer networks for pedestrian trajectory prediction. Appl Sci Basel 13(5):3296","journal-title":"Appl Sci Basel"},{"issue":"4","key":"7304_CR5","doi-asserted-by":"publisher","first-page":"8799","DOI":"10.1109\/LRA.2022.3188101","volume":"7","author":"J Qiu","year":"2022","unstructured":"Qiu J, Chen L, Gu X, Lo FPW, Tsai YY, Sun J, Liu J, Lo B (2022) Egocentric human trajectory forecasting with a wearable camera and multi-modal fusion. IEEE Robot Autom Lett 7(4):8799\u20138806","journal-title":"IEEE Robot Autom Lett"},{"issue":"8","key":"7304_CR6","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1177\/0278364920917446","volume":"39","author":"A Rudenko","year":"2020","unstructured":"Rudenko A, Palmieri L, Herman M, Kitani KM, Gavrila DM, Arras KO (2020) Human motion trajectory prediction: a survey. Int J Robot Res 39(8):895\u2013935","journal-title":"Int J Robot Res"},{"key":"7304_CR7","doi-asserted-by":"crossref","unstructured":"Yagi T, Mangalam K, Yonetani R, Sato Y (2018) Future person localization in first-person videos. In: IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00792"},{"issue":"2","key":"7304_CR8","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1109\/LRA.2021.3056339","volume":"6","author":"Y Yao","year":"2021","unstructured":"Yao Y, Atkins E, Johnson-Roberson M (2021) Bitrap: Bi-directional pedestrian trajectory prediction with multi-modal goal estimation. IEEE Robot Autom Lett 6(2):1463\u20131470","journal-title":"IEEE Robot Autom Lett"},{"issue":"2","key":"7304_CR9","doi-asserted-by":"publisher","first-page":"2716","DOI":"10.1109\/LRA.2022.3145090","volume":"7","author":"C Wang","year":"2022","unstructured":"Wang C, Wang Y, Xu DJM (2022) adn Crandal: Stepwise goal-driven networks for trajectory prediction. IEEE Robot Autom Lett 7(2):2716\u20132723","journal-title":"IEEE Robot Autom Lett"},{"key":"7304_CR10","doi-asserted-by":"crossref","unstructured":"Salzmann T, Ivanovic B, Chakravarty P, Pavone M (2020) Trajectron++: dynamically feasible trajectory Forecasting with heterogeneous Data. In: Paper presented at European conference on computer vision","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"7304_CR11","doi-asserted-by":"crossref","unstructured":"Rasouli A, Kotseruba I, Kunic T, Tsotsos J (2019) PIE: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2019.00636"},{"key":"7304_CR12","doi-asserted-by":"crossref","unstructured":"Halawa M, Hellwich O, Bideau (2022) Action-based contrastive learning for trajectory prediction. In: European conference on computer vision","DOI":"10.1007\/978-3-031-19842-7_9"},{"key":"7304_CR13","doi-asserted-by":"crossref","unstructured":"Tsai Y, Bai SJ, Liang PP, Kolter JZ, Morency LP, Salakhutdinov R (2019) Multimodal transformer for unaligned multimodal language sequences. In: 57th annual meeting of the association-for-computational-linguistics (ACL)","DOI":"10.18653\/v1\/P19-1656"},{"key":"7304_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109372","volume":"138","author":"L Franco","year":"2023","unstructured":"Franco L, Placidi L, Giuliari F, Hasan I, Cristani M, Galasso F (2023) Under the hood of transformer networks for trajectory forecasting. Pattern Recog 138:109372","journal-title":"Pattern Recog"},{"key":"7304_CR15","unstructured":"Sang H, Chen W, Wang H, Wang J Mstcnn: multi-modal spatio-temporal convolutional neural network for pedestrian trajectory prediction"},{"key":"7304_CR16","doi-asserted-by":"crossref","unstructured":"Alikadic A, Saito H, Hachiuma R (2022) Transformer networks for future person localization in first-person videos. Advances in Visual Computing. In: ISVC 2022. Lecture notes in computer science","DOI":"10.1007\/978-3-031-20716-7_14"},{"key":"7304_CR17","unstructured":"Vaswani A, Shazeer N, Parmar N (2017) Attention is all you need. In: Conference and workshop on neural information processing systems"},{"key":"7304_CR18","doi-asserted-by":"crossref","unstructured":"Guo JY, Han K, Wu H, Tang YH, Chen XH, Wang YH, Xu C (2022) CMT: convolutional neural networks meet vision transformers. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR52688.2022.01186"},{"key":"7304_CR19","doi-asserted-by":"crossref","unstructured":"Wu K, Peng H, Chen M, Fu J, Chao H (2021) Rethinking and improving relative position encoding for vision transformer. In: Paper presented at the 18th IEEE\/CVF international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV48922.2021.00988"},{"key":"7304_CR20","doi-asserted-by":"crossref","unstructured":"Gupta A, Johnson J, Fei-Fei L (2018) Social GAN: socially acceptable trajectories with generative adversarial networks. In: IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"7304_CR21","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-69532-3_25","volume":"12623","author":"P Dendorfer","year":"2021","unstructured":"Dendorfer P, O\u0161ep A, Leal-Taix\u00e9 L (2021) Goal-gan: Multimodal trajectory prediction based on goal position estimation. Lecture Notes Comput Sci 12623:405\u2013420","journal-title":"Lecture Notes Comput Sci"},{"key":"7304_CR22","unstructured":"Sohn K, Yan XC, Lee H (2015) Learning Structured Output Representation using Deep Conditional Generative Models. 29th Annual Conference on Neural Information Processing Systems (NIPS)"},{"key":"7304_CR23","doi-asserted-by":"crossref","unstructured":"Gu TP, Chen GY, Li JL (2022) Stochastic trajectory prediction via motion indeterminacy diffusion. In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR52688.2022.01660"},{"key":"7304_CR24","doi-asserted-by":"crossref","unstructured":"Su Z, Huang G, Zhang SY, Hua W (2022) Crossmodal transformer based generative framework for pedestrian trajectory prediction. In: IEEE International Conference on Robotics and Automation (ICRA)","DOI":"10.1109\/ICRA46639.2022.9812226"},{"key":"7304_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120077","volume":"225","author":"S Ahmed","year":"2023","unstructured":"Ahmed S, Al Bazi A, Saha C, Rajbhandari S, Huda MN (2023) Multi-scale pedestrian intent prediction using 3d joint information as spatio-temporal representation. Expert Syst Appl 225:120077","journal-title":"Expert Syst Appl"},{"key":"7304_CR26","doi-asserted-by":"crossref","unstructured":"Bhattacharyya A, Fritz M, Schiele B (2018) Long-term on-board prediction of people in traffic scenes under uncertainty. In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2018.00441"},{"key":"7304_CR27","doi-asserted-by":"crossref","unstructured":"Rasouli A, Kotseruba I (2022) Pedformer: Pedestrian bhavior prediction via cross-modal attention modulation and gated multitask learning. arXiv","DOI":"10.1109\/ICRA48891.2023.10161318"},{"issue":"1","key":"7304_CR28","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s12204-023-2565-3","volume":"28","author":"J Fu","year":"2023","unstructured":"Fu J, Zhao X (2023) Action-aware encoder-decoder network for pedestrian trajectory prediction. J Shanghai Jiaotong Univ 28(1):20\u201327","journal-title":"J Shanghai Jiaotong Univ"},{"issue":"2","key":"7304_CR29","doi-asserted-by":"publisher","first-page":"2679","DOI":"10.1109\/TITS.2024.3503683","volume":"26","author":"C Hu","year":"2025","unstructured":"Hu C, Niu R, Lin Y, Yang B, Chen H, Zhao B, Zhang X (2025) Probabilistic trajectory prediction of vulnerable road user using multimodal inputs. IEEE Trans Intell Transp Syst 26(2):2679\u20132689","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"7304_CR30","doi-asserted-by":"publisher","first-page":"8043","DOI":"10.1109\/TASE.2024.3476382","volume":"22","author":"L Astuti","year":"2024","unstructured":"Astuti L, Lin Y, Chen W (2024) Chen: Predicting vulnerable road user behavior with transformer-based gumbel distribution networks. IEEE Trans Autom Sci Eng 22:8043\u20138056","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"7304_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104110","volume":"107","author":"RP Wang","year":"2021","unstructured":"Wang RP, Cui X, Song Y, Chen K, Fang H (2021) Multi-information-based convolutional neural network with attention mechanism for pedestrian trajectory prediction. Image C Visionomput 107:104110","journal-title":"Image C Visionomput"},{"key":"7304_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2023.104671","volume":"134","author":"K Chen","year":"2023","unstructured":"Chen K, Zhu HH, Tang DB, Zheng K (2023) Future pedestrian location prediction in first-person videos for autonomous vehicles and social robots. Image Vis Comput 134:104671","journal-title":"Image Vis Comput"},{"key":"7304_CR33","doi-asserted-by":"crossref","unstructured":"Yao Y, Xu MZ, Choi C, Crandall DJ, Atkins EM, Dariush B (2019) Egocentric vision-based future vehicle localization for intelligent driving assistance systems. In: IEEE International Conference on Robotics and Automation (ICRA)","DOI":"10.1109\/ICRA.2019.8794474"},{"key":"7304_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.107993","volume":"133","author":"J Wang","year":"2024","unstructured":"Wang J, Sang H, Liu Q, Chen W, Zhao Z (2024) Neural differential constraint-based pedestrian trajectory prediction model in ego-centric perspective. Eng Appl Artif Intell 133:107993","journal-title":"Eng Appl Artif Intell"},{"key":"7304_CR35","doi-asserted-by":"crossref","unstructured":"Chiara LF, Coscia P, Das S, Calderara S, Cucchiara R, Ballan L (2022) Goal-driven self-attentive recurrent networks for trajectory prediction. In: IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPRW56347.2022.00282"},{"key":"7304_CR36","doi-asserted-by":"crossref","unstructured":"Mangalam K, An Y, Girase H, Malik J (2021) From goals, Waypoints paths to long term human trajectory forecasting. In: IEEE\/CVF International Conference on Computer Vision","DOI":"10.1109\/ICCV48922.2021.01495"},{"key":"7304_CR37","doi-asserted-by":"crossref","unstructured":"Neumann L, Vedaldi A (2021) Pedestrian and ego-vehicle trajectory prediction from monocular camera. In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR46437.2021.01007"},{"issue":"2","key":"7304_CR38","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TIV.2022.3162719","volume":"7","author":"DF Yang","year":"2022","unstructured":"Yang DF, Zhang HL, Yurtsever E, Redmill KA, Ozguner U (2022) Predicting pedestrian crossing intention with feature fusion and spatio-temporal attention. IEEE Trans Intell Vehicles 7(2):221\u2013230","journal-title":"IEEE Trans Intell Vehicles"},{"key":"7304_CR39","unstructured":"Fofanah AJ, Chen D, Wen L, Zhang X Chamformer: Dual heterogeneous three-stages coupling and multivariate feature-aware learning network for traffic flow forecasting"},{"key":"7304_CR40","unstructured":"Fofanah AJ, Wen L, Chen X, Zhang S Stalformer: Advanced traffic flow prediction using graph-based spatio-temporal transformer and augmented feature learning"},{"key":"7304_CR41","unstructured":"Huynh M, laghband G (2021) Gprar: Graph convolutional network based pose reconstruction and action recognition for human trajectory prediction. arXiv"},{"key":"7304_CR42","doi-asserted-by":"crossref","unstructured":"Heidari N, Iosifidis A (2021) Progressive Spatio-Temporal graph convolutional network for skeleton-based human action recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","DOI":"10.1109\/ICASSP39728.2021.9413860"},{"key":"7304_CR43","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2D Pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2017.143"},{"key":"7304_CR44","doi-asserted-by":"crossref","unstructured":"Xu HF, Zhang J, Cai JF, Rezatofighi H, Tao DC (2022) GMFlow: Learning optical flow via global matching. In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR52688.2022.00795"},{"key":"7304_CR45","unstructured":"Bai S, Kolter J, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv"},{"key":"7304_CR46","doi-asserted-by":"crossref","unstructured":"He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: Paper Presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"7304_CR47","unstructured":"Giuliari F, Hasan I, Cristani M, Galasso F (2011) Transformer networks for trajectory forecasting. In: 25th International Conference on Pattern Recognition (ICPR)"},{"issue":"2","key":"7304_CR48","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1109\/LRA.2019.2895266","volume":"4","author":"XX Do","year":"2019","unstructured":"Do XX, Vasudevan M, Johnson-Roberson M (2019) Bio-lstm: A biomechanically inspired recurrent neural network for 3-d pedestrian pose and gait prediction. IEEE Robot Autom Lett 4(2):1501\u20131508","journal-title":"IEEE Robot Autom Lett"},{"key":"7304_CR49","doi-asserted-by":"crossref","unstructured":"Yao Y, Xu MZ, Wang YC, Crandall DJ, Atkins EM (2019) Unsupervised traffic accident detection in first-person videos. In: IEEE International Conference on Intelligent Robots and Systems","DOI":"10.1109\/IROS40897.2019.8967556"},{"key":"7304_CR50","doi-asserted-by":"crossref","unstructured":"Alahi A, Goel K, Ramanathan V, Robicquet A, Li FF, Savarese S (2016) Social LSTM: human trajectory prediction in crowded spaces. In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2016.110"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07304-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07304-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07304-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T12:00:41Z","timestamp":1746273641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07304-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,3]]},"references-count":50,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["7304"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07304-9","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,3]]},"assertion":[{"value":"8 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"819"}}