{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:13:42Z","timestamp":1778285622676,"version":"3.51.4"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52372380"],"award-info":[{"award-number":["52372380"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114748","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T14:51:52Z","timestamp":1775659912000},"page":"114748","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Graph channel receptive field transformer for multi-agent trajectory prediction"],"prefix":"10.1016","volume":"176","author":[{"given":"Jiankun","family":"Peng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiakang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunye","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114748_bib1","series-title":"2008 IEEE Intelligent Vehicles Symposium. Presented at the 2008 IEEE Intelligent Vehicles Symposium","first-page":"1068","article-title":"Where will the oncoming vehicle be the next second?","author":"Barth","year":"2008"},{"key":"10.1016\/j.engappai.2026.114748_bib2","series-title":"Spectral Networks and Locally Connected Networks on Graphs","author":"Bruna","year":"2014"},{"key":"10.1016\/j.engappai.2026.114748_bib3","series-title":"2020 IEEE International Conference on Robotics and Automation (ICRA)","first-page":"9491","article-title":"Spagnn: spatially-aware graph neural networks for relational behavior forecasting from sensor data","author":"Casas","year":"2020"},{"key":"10.1016\/j.engappai.2026.114748_bib4","author":"Chai"},{"key":"10.1016\/j.engappai.2026.114748_bib5","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8748","article-title":"Argoverse: 3d tracking and forecasting with rich maps","author":"Chang","year":"2019"},{"key":"10.1016\/j.engappai.2026.114748_bib6","series-title":"2019 International Conference on Robotics and Automation (Icra)","first-page":"2090","article-title":"Multimodal trajectory predictions for autonomous driving using deep convolutional networks","author":"Cui","year":"2019"},{"key":"10.1016\/j.engappai.2026.114748_bib7","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6797","article-title":"Tpnet: trajectory proposal network for motion prediction","author":"Fang","year":"2020"},{"key":"10.1016\/j.engappai.2026.114748_bib8","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11525","article-title":"Vectornet: encoding hd maps and agent dynamics from vectorized representation","author":"Gao","year":"2020"},{"key":"10.1016\/j.engappai.2026.114748_bib9","series-title":"International Conference on Machine Learning","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"key":"10.1016\/j.engappai.2026.114748_bib10","series-title":"2022 International Conference on Robotics and Automation (ICRA)","first-page":"9107","article-title":"Gohome: graph-oriented heatmap output for future motion estimation","author":"Gilles","year":"2022"},{"key":"10.1016\/j.engappai.2026.114748_bib11","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"15303","article-title":"Densetnt: end-to-end trajectory prediction from dense goal sets","author":"Gu","year":"2021"},{"key":"10.1016\/j.engappai.2026.114748_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110799","article-title":"A hypergraph-based dual-path multi-agent trajectory prediction model with topology inferring","volume":"152","author":"Hu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114748_bib13","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8454","article-title":"Rules of the road: predicting driving behavior with a convolutional model of semantic interactions","author":"Hong","year":"2019"},{"issue":"11","key":"10.1016\/j.engappai.2026.114748_bib14","doi-asserted-by":"crossref","first-page":"6607","DOI":"10.1109\/TCYB.2024.3412149","article-title":"Flow2GNN: flexible two-way flow message passing for enhancing GNNs beyond homophily","volume":"54","author":"Huang","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114748_bib15","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"6272","article-title":"Stgat: modeling spatial-temporal interactions for human trajectory prediction","author":"Huang","year":"2019"},{"key":"10.1016\/j.engappai.2026.114748_bib16","doi-asserted-by":"crossref","first-page":"13860","DOI":"10.1109\/TPAMI.2023.3298301","article-title":"Hdgt: heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding","volume":"45","author":"Jia","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.114748_bib17","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"10.1016\/j.engappai.2026.114748_bib18","series-title":"2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","first-page":"5784","article-title":"End-to-end contextual perception and prediction with interaction transformer","author":"Li","year":"2020"},{"key":"10.1016\/j.engappai.2026.114748_bib19","series-title":"Computer Vision \u2013 ECCV 2020, Lecture Notes in Computer Science","first-page":"541","article-title":"Learning Lane graph representations for motion forecasting","author":"Liang","year":"2020"},{"key":"10.1016\/j.engappai.2026.114748_bib20","series-title":"DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR","author":"Liu","year":"2022"},{"issue":"9","key":"10.1016\/j.engappai.2026.114748_bib21","doi-asserted-by":"crossref","first-page":"8720","DOI":"10.1109\/TVT.2021.3098429","article-title":"Trajectory prediction of preceding target vehicles based on Lane crossing and final points generation model considering driving styles","volume":"70","author":"Liu","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.engappai.2026.114748_bib22","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7577","article-title":"Multimodal motion prediction with stacked transformers","author":"Liu","year":"2021"},{"key":"10.1016\/j.engappai.2026.114748_bib23","article-title":"Predicting vehicle trajectory of non-lane based driving behaviour with temporal fusion transformer","volume":"12","author":"Long","year":"2024","journal-title":"Transport. Bus.: Transport Dynamics"},{"key":"10.1016\/j.engappai.2026.114748_bib24","series-title":"SGDR: Stochastic Gradient Descent with Warm Restarts","author":"Loshchilov","year":"2017"},{"key":"10.1016\/j.engappai.2026.114748_bib25","series-title":"Decoupled Weight Decay Regularization","author":"Loshchilov","year":"2019"},{"key":"10.1016\/j.engappai.2026.114748_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.130882","article-title":"HVAE-DC: a hierarchical variational autoencoder-based deep clustering model for multi-level driving behavior","volume":"305","author":"Ma","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114748_bib27","series-title":"Computer Vision \u2013 ECCV 2020, Lecture Notes in Computer Science","first-page":"759","article-title":"It is not the journey but the destination: endpoint conditioned trajectory prediction","author":"Mangalam","year":"2020"},{"key":"10.1016\/j.engappai.2026.114748_bib28","author":"Ngiam"},{"key":"10.1016\/j.engappai.2026.114748_bib29","first-page":"1","article-title":"RM 2Occ: Re-projection multi-task multi-sensor fusion for autonomous driving 3D object detection and occupancy perception","author":"Ren","year":"2025","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"10.1016\/j.engappai.2026.114748_bib30","doi-asserted-by":"crossref","first-page":"17451","DOI":"10.1109\/ACCESS.2022.3149269","article-title":"A method for predicting diverse lane-changing trajectories of surrounding vehicles based on early detection of Lane change","volume":"10","author":"Ren","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114748_bib31","series-title":"Computer Vision \u2013 ECCV 2020, Lecture Notes in Computer Science","first-page":"683","article-title":"Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data","author":"Salzmann","year":"2020"},{"key":"10.1016\/j.engappai.2026.114748_bib32","first-page":"6531","article-title":"Motion transformer with global intention localization and local movement refinement","volume":"35","author":"Shi","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114748_bib33","doi-asserted-by":"crossref","first-page":"3955","DOI":"10.1109\/TPAMI.2024.3352811","article-title":"Mtr++: multi-agent motion prediction with symmetric scene modeling and guided intention querying","volume":"46","author":"Shi","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.114748_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127974","article-title":"Modeling lane-changing spatiotemporal features based on the driving behavior generation mechanism of human drivers","volume":"284","author":"Song","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114748_bib35","series-title":"2022 International Conference on Robotics and Automation (ICRA)","first-page":"7814","article-title":"Multipath++: efficient information fusion and trajectory aggregation for behavior prediction","author":"Varadarajan","year":"2022"},{"key":"10.1016\/j.engappai.2026.114748_bib36","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114748_bib37","doi-asserted-by":"crossref","first-page":"3060","DOI":"10.4271\/2020-01-0112","article-title":"Vehicle trajectory prediction based on motion model and maneuver model fusion with interactive multiple models","volume":"2","author":"Xiao","year":"2020","journal-title":"SAE International Journal of Advances and Current Practices in Mobility"},{"key":"10.1016\/j.engappai.2026.114748_bib38","series-title":"2014 IEEE International Conference on Robotics and Automation (ICRA)","first-page":"2507","article-title":"Motion planning under uncertainty for on-road autonomous driving","author":"Xu","year":"2014"},{"key":"10.1016\/j.engappai.2026.114748_bib39","doi-asserted-by":"crossref","first-page":"9190","DOI":"10.1109\/JIOT.2021.3093523","article-title":"GSAN: graph self-attention network for learning spatial\u2013temporal interaction representation in autonomous driving","volume":"9","author":"Ye","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.engappai.2026.114748_bib40","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11318","article-title":"Tpcn: temporal point cloud networks for motion forecasting","author":"Ye","year":"2021"},{"key":"10.1016\/j.engappai.2026.114748_bib41","series-title":"Vehicle Trajectory Prediction Based on Hidden Markov Model","author":"Ye","year":"2016"},{"key":"10.1016\/j.engappai.2026.114748_bib42","series-title":"Computer Vision \u2013 ECCV 2020, Lecture Notes in Computer Science","first-page":"507","article-title":"Spatio-temporal graph transformer networks for pedestrian trajectory prediction","author":"Yu","year":"2020"},{"key":"10.1016\/j.engappai.2026.114748_bib43","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"9813","article-title":"Agentformer: agent-aware transformers for socio-temporal multi-agent forecasting","author":"Yuan","year":"2021"},{"key":"10.1016\/j.engappai.2026.114748_bib44","series-title":"Computer Vision \u2013 ECCV 2022, Lecture Notes in Computer Science","first-page":"659","article-title":"MOTR: end-to-end multiple-object tracking with transformer","author":"Zeng","year":"2022"},{"key":"10.1016\/j.engappai.2026.114748_bib45","series-title":"Conference on Robot Learning","first-page":"895","article-title":"Tnt: target-driven trajectory prediction","author":"Zhao","year":"2021"},{"key":"10.1016\/j.engappai.2026.114748_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110125","article-title":"Heterogeneous hypergraph transformer network with cross-modal future interaction for multi-agent trajectory prediction","volume":"144","author":"Zhou","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114748_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.113335","article-title":"Multi-agent trajectory prediction with hierarchical coordinate-based representation","volume":"165","author":"Zhu","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114748_bib48","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8823","article-title":"Hivt: hierarchical vector transformer for multi-agent motion prediction","author":"Zhou","year":"2022"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010304?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010304?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:33:51Z","timestamp":1778283231000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626010304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":48,"alternative-id":["S0952197626010304"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114748","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Graph channel receptive field transformer for multi-agent trajectory prediction","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114748","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114748"}}