{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:48:29Z","timestamp":1791953309852,"version":"4.3.4"},"reference-count":37,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In highly dynamic and interactive vehicular environments, accurate and efficient trajectory prediction is critical for driving safety and traffic efficiency. To overcome the limitations of existing methods, including degradation of long-range historical context, high computational cost for long sequences, attention dispersion in spatial interactions, and limited explicit modeling of future interactions, this paper proposes a Hierarchical Mamba Graph Attention Network (HM-GAT) for vehicle trajectory prediction. A Mamba-based selective state-space model (SSM) encoder with linear complexity is employed to capture nonlinear temporal dynamics and transient maneuvering behaviors. For spatial modeling, a heterogeneous topology-driven dual-layer graph attention mechanism, combined with adaptive sparse filtering and a foresighted future graph, enables precise identification of neighboring vehicles with potential collision risks. The proposed architecture integrates global intention exploration, topology bridging, and residual refinement in a coarse-to-fine manner, achieving effective spatio-temporal decoupling. Experimental results show that HM-GAT reduces the average RMSE by 33.1% relative to the best-performing baseline on NGSIM, achieves competitive prediction accuracy on Argoverse 2 without HD-map priors, and achieves lower inference latency than the compared baseline methods on both datasets.<\/jats:p>","DOI":"10.3390\/s26196332","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T16:43:38Z","timestamp":1791564218000},"page":"6332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HM-GAT: Vehicle Trajectory Prediction Based on Hierarchical Mamba and Graph Attention Networks"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9498-3471","authenticated-orcid":false,"given":"Qiong","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingde","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Hull, Hull HU 67RX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TITS.2020.3012034","article-title":"Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review","volume":"23","author":"Mozaffari","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TIV.2022.3167103","article-title":"A Survey on Trajectory-Prediction Methods for Autonomous Driving","volume":"7","author":"Huang","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yin, C., Cecotti, M., Auger, D.J., Fotouhi, A., and Jiang, H. (2025). Deep-learning-based vehicle trajectory prediction: A review. IET Intell. Transp. Syst., 19.","DOI":"10.1049\/itr2.70001"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103588","DOI":"10.1016\/j.inffus.2025.103588","article-title":"Trajectory prediction for autonomous driving: Progress, limitations, and future directions","volume":"126","author":"Madjid","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_5","first-page":"100733","article-title":"Machine learning for autonomous vehicle\u2019s trajectory prediction: A comprehensive survey, challenges, and future research directions","volume":"46","author":"Bharilya","year":"2024","journal-title":"Veh. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/LRA.2020.3032079","article-title":"Interaction-Aware Motion Prediction for Autonomous Driving: A Multiple Model Kalman Filtering Scheme","volume":"6","author":"Lefkopoulos","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, P., Qu, T., Gao, H., and Gong, X. (2023). Driving intention recognition of surrounding vehicles based on a time-sequenced weights hidden Markov model for autonomous driving. Sensors, 23.","DOI":"10.3390\/s23218761"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108662","DOI":"10.1016\/j.engfailanal.2024.108662","article-title":"A deep learning fault diagnosis method for metro on-board detection on rail corrugation","volume":"164","author":"Wang","year":"2024","journal-title":"Eng. Fail. Anal."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Han, P., Roop, P., Liu, J., Bao, T., and Wang, Y. (2023). STF: Spatial Temporal Fusion for Trajectory Prediction. arXiv.","DOI":"10.1109\/M2VIP58386.2023.10413434"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lee, D., Li, Y., Kim, Y., Xiao, S., and Panda, P. (2025, January 10\u201317). Spiking Transformer with Spatial-Temporal Attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01302"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pazho, A.D., Noghre, G.A., Katariya, V., and Tabkhi, H. (2024, January 17\u201321). VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW63382.2024.00574"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shi, S., Jiang, L., Dai, D., and Schiele, B. (December, January 28). Motion Transformer with Global Intention Localization and Local Movement Refinement. Proceedings of the Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, LA, USA.","DOI":"10.52202\/068431-0473"},{"key":"ref_13","first-page":"294","article-title":"Traffic Flow Prediction with Vehicle Trajectories","volume":"35","author":"Li","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, K., Zhao, X., Huang, Y., Fang, G., Song, X., Wang, R., and Wang, Z. (2025, January 10\u201317). SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory Prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.02092"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qin, X., Lu, C., Wei, Y., Fan, S., Song, Z., and Gong, J. (2025, January 19). STGT-Gen: Spatio-Temporal Graph Transformer for Multi-Vehicle Traffic Scenario Generation. Proceedings of the SAE 2025 Intelligent and Connected Vehicles Symposium, Shanghai, China.","DOI":"10.4271\/2025-01-7316"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7222","DOI":"10.1109\/TVT.2025.3627215","article-title":"DiffMATP: Interaction-Aware Multi-Agent Trajectory Prediction via Denoising Diffusion Models","volume":"75","author":"Li","year":"2025","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, R., Xu, H., Gong, G., Kuang, Y., and Liu, Z. (2022). Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11070354"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"18927","DOI":"10.1109\/TITS.2022.3172015","article-title":"DeepTrack: Lightweight deep learning for vehicle trajectory prediction in highways","volume":"23","author":"Katariya","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, J., and Wang, Y. (2024, January 1\u20133). Deep Learning-Based Vehicle Trajectory Prediction in the Internet of Vehicles with LSTM Models. Proceedings of the 7th International Conference on Information Technologies and Electrical Engineering (ICITEE \u201824), Guangzhou, China.","DOI":"10.1145\/3717934.3717954"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, J., Yan, K., Xia, X., and Yang, B. (2025). A survey of deep learning-based pedestrian trajectory prediction: Challenges and solutions. Sensors, 25.","DOI":"10.3390\/s25030957"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Deo, N., and Trivedi, M.M. (2018, January 18\u201322). Convolutional Social Pooling for Vehicle Trajectory Prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00196"},{"key":"ref_22","unstructured":"Chen, W., Wang, F., and Sun, H. (2021, January 17\u201319). S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving. Proceedings of the Asian Conference on Machine Learning (ACML), Virtual."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17080","DOI":"10.1109\/TITS.2025.3588228","article-title":"Interaction-Aware Trajectory Prediction for Safe Motion Planning in Autonomous Driving: A Transformer-Transfer Learning Approach","volume":"26","author":"Liang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems 30 (NeurIPS 2017), Curran Associates, Inc."},{"key":"ref_25","unstructured":"Gu, A., and Dao, T. (2023). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, Y., Cheng, Y., and Wang, K. (2025, January 11\u201315). Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01126"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, S., Liu, C., Xu, X., Yeo, S.Y., and Yang, X. (2025, January 19\u201323). Future-Aware Interaction Network for Motion Forecasting. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Honolulu, HI, USA.","DOI":"10.1109\/ICCV51701.2025.00704"},{"key":"ref_28","first-page":"1490","article-title":"GAMDTP: Dynamic Trajectory Prediction with Graph Attention Mamba Network","volume":"50","author":"Liu","year":"2026","journal-title":"J. Intell. Fuzzy Syst. Appl. Eng. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, X., Ying, X., and Chuah, M.C. (2020). GRIP++: Enhanced Graph-Based Interaction-Aware Trajectory Prediction for Autonomous Driving. arXiv.","DOI":"10.1109\/ITSC.2019.8917228"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"17654","DOI":"10.1109\/TITS.2022.3155749","article-title":"Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving","volume":"23","author":"Sheng","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_31","first-page":"197","article-title":"Vehicle Trajectory Prediction Using LSTMs with Spatial-Temporal Attention Mechanisms","volume":"14","author":"Lin","year":"2022","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5339","DOI":"10.1109\/TIV.2023.3321656","article-title":"DACR-AMTP: Adaptive Multi-Modal Vehicle Trajectory Prediction for Dynamic Drivable Areas Based on Collision Risk","volume":"9","author":"Cong","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2171","DOI":"10.1109\/TIV.2023.3319024","article-title":"Trajectory Prediction Network of Autonomous Vehicles With Fusion of Historical Interactive Features","volume":"9","author":"Zuo","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"12025","DOI":"10.1109\/TITS.2025.3556087","article-title":"Interaction-Aware Trajectory Prediction Method Based on Sparse Spatial-Temporal Transformer for Internet of Vehicles","volume":"26","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Song, H., Ding, W., Chen, Y., Shen, S., Wang, M.Y., and Chen, Q. (2020, January 23\u201328). PiP: Planning-informed Trajectory Prediction for Autonomous Driving. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58589-1_36"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4632","DOI":"10.1109\/TIV.2024.3376074","article-title":"A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving","volume":"9","author":"Liao","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Wang, J., Li, Y.-H., and Huang, Y.-K. (2023). Query-Centric Trajectory Prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 17\u201324 June 2023, IEEE.","DOI":"10.1109\/CVPR52729.2023.01713"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6332\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:16:25Z","timestamp":1791951385000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6332"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,7]]},"references-count":37,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196332"],"URL":"https:\/\/doi.org\/10.3390\/s26196332","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,7]]}}}