{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:53:06Z","timestamp":1774965186249,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52202495"],"award-info":[{"award-number":["52202495"]}]},{"name":"National Natural Science Foundation of China","award":["52202494"],"award-info":[{"award-number":["52202494"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships is crucial for accurate trajectory prediction. In this context, we propose a goal-guided and interaction-aware state refinement graph attention network (SRGAT) for multi-agent trajectory prediction. This model effectively integrates high-precision map data and dynamic traffic states and captures long-term temporal dependencies through the Transformer network. Based on these dependencies, it generates multiple potential goals and Points of Interest (POIs). Through its dual-branch, multimodal prediction approach, the model not only proposes various plausible future trajectories associated with these POIs, but also rigorously assesses the confidence levels of each trajectory. This goal-oriented strategy enables SRGAT to accurately predict the future movement trajectories of other vehicles in complex traffic scenarios. Tested on the Argoverse and nuScenes datasets, SRGAT surpasses existing algorithms in key performance metrics by adeptly integrating past trajectories and current context. This goal-guided approach not only enhances long-term prediction accuracy, but also ensures its reliability, demonstrating a significant advancement in trajectory forecasting.<\/jats:p>","DOI":"10.3390\/s24072065","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:32:36Z","timestamp":1711369956000},"page":"2065","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Goal-Guided Graph Attention Network with Interactive State Refinement for Multi-Agent Trajectory Prediction"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2383-3200","authenticated-orcid":false,"given":"Jianghang","family":"Wu","sequence":"first","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4675-5833","authenticated-orcid":false,"given":"Senyao","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haocheng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyu","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4195-5033","authenticated-orcid":false,"given":"Fei","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"},{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8083-6403","authenticated-orcid":false,"given":"Hongyu","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1597-1961","authenticated-orcid":false,"given":"Rui","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"ref_1","unstructured":"Conde, M.V., Barea, R., Bergasa, L.M., and G\u00f3mez-Hu\u00e9lamo, C. (2023, January 17\u201324). Improving Multi-Agent Motion Prediction With Heuristic Goals and Motion Refinement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chang, M.F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., and Ramanan, D. (2019, January 16\u201317). Argoverse: 3D tracking and forecasting with rich maps. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C.R., and Zhou, Y. (2021, January 11\u201317). Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00957"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., and Urtasun, R. (2019, January 15\u201320). End-to-end interpretable neural motion planner. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00886"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/TITS.2019.2955721","article-title":"Target vehicle motion prediction-based motion planning framework for autonomous driving in uncontrolled intersections","volume":"22","author":"Jeong","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Frasch, J.V., Gray, A., Zanon, M., Ferreau, H.J., Sager, S., Borrelli, F., and Diehl, M. (2013, January 17\u201319). An auto-generated nonlinear MPC algorithm for real-time obstacle avoidance of ground vehicles. Proceedings of the 2013 European Control Conference (ECC), Zurich, Switzerland.","DOI":"10.23919\/ECC.2013.6669836"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gindele, T., Brechtel, S., and Dillmann, R. (2010, January 19\u201322). A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal.","DOI":"10.1109\/ITSC.2010.5625262"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Schreier, M., Willert, V., and Adamy, J. (2014, January 8\u201311). Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems. Proceedings of the 17th international IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957713"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIV.2018.2804159","article-title":"How would surround vehicles move? a unified framework for maneuver classification and motion prediction","volume":"3","author":"Deo","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1111\/mice.12595","article-title":"A generative adversarial network for travel times imputation using trajectory data","volume":"36","author":"Zhang","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1109\/ACCESS.2023.3345643","article-title":"An Enhanced Vehicle Trajectory Prediction Model Leveraging LSTM and Social-Attention Mechanisms","volume":"12","author":"Qiao","year":"2023","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., and Savarese, S. (2016, January 27\u201330). Social lstm: Human trajectory prediction in crowded spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref_13","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 conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00196"},{"key":"ref_14","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_15","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TIV.2020.2991952","article-title":"Attention based vehicle trajectory prediction","volume":"6","author":"Messaoud","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Messaoud, K., Deo, N., Trivedi, M.M., and Nashashibi, F. (2021, January 11\u201317). Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.","DOI":"10.1109\/IV48863.2021.9576054"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10279","DOI":"10.1109\/TITS.2023.3281393","article-title":"Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review","volume":"24","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Syed, A., and Morris, B. (2021, January 11\u201317). STGT: Forecasting pedestrian motion using spatio-temporal graph transformer. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.","DOI":"10.1109\/IV48863.2021.9575498"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mercat, J., Gilles, T., El Zoghby, N., Sandou, G., Beauvois, D., and Gil, G.P. (August, January 31). Multi-head attention for multi-modal joint vehicle motion forecasting. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197340"},{"key":"ref_21","unstructured":"Girgis, R., Golemo, F., Codevilla, F., Weiss, M., D\u2019Souza, J.A., Kahou, S.E., Heide, F., and Pal, C. (2021). Latent variable sequential set transformers for joint multi-agent motion prediction. arXiv."},{"key":"ref_22","unstructured":"Zhao, H., Gao, J., Lan, T., Sun, C., Sapp, B., Varadarajan, B., Shen, Y., Shen, Y., Chai, Y., and Schmid, C. (2021, January 8\u201311). Tnt: Target-driven trajectory prediction. Proceedings of the Conference on Robot Learning, London, UK."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Choi, S., Kim, J., Yun, J., and Choi, J.W. (2023, January 2\u20136). R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00783"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cui, H., Radosavljevic, V., Chou, F.C., Lin, T.H., Nguyen, T., Huang, T.K., Schneider, J., and Djuric, N. (2019, January 20\u201324). Multimodal trajectory predictions for autonomous driving using deep convolutional networks. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793868"},{"key":"ref_25","unstructured":"Chai, Y., Sapp, B., Bansal, M., and Anguelov, D. (2019). Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. arXiv."},{"key":"ref_26","unstructured":"Casas, S., Luo, W., and Urtasun, R. (2018, January 29\u201331). Intentnet: Learning to predict intention from raw sensor data. Proceedings of the Conference on Robot Learning, Z\u00fcrich, Switzerland."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hong, J., Sapp, B., and Philbin, J. (2019, January 15\u201320). Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00865"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhu, X., Yu, C., Li, W., Ma, Y., Jin, R., Ren, X., Ren, D., Wang, M., and Yang, W. (June, January 29). Ganet: Goal area network for motion forecasting. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK.","DOI":"10.1109\/ICRA48891.2023.10160468"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gao, J., Sun, C., Zhao, H., Shen, Y., Anguelov, D., Li, C., and Schmid, C. (2020, January 14\u201319). Vectornet: Encoding hd maps and agent dynamics from vectorized representation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"ref_30","unstructured":"Shi, S., Jiang, L., Dai, D., and Schiele, B. (2024). IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liang, M., Yang, B., Hu, R., Chen, Y., Liao, R., Feng, S., and Urtasun, R. (2020, January 23\u201328). Learning lane graph representations for motion forecasting. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part II 16.","DOI":"10.1007\/978-3-030-58536-5_32"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_33","unstructured":"Kim, W., Son, B., and Kim, I. (2021, January 18\u201324). Vilt: Vision-and-language transformer without convolution or region supervision. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 14\u201319). nuscenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zeng, W., Liang, M., Liao, R., and Urtasun, R. (October, January 27). Lanercnn: Distributed representations for graph-centric motion forecasting. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636035"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., Su, T., Da, F., and Yang, X. (2023, January 17\u201324). ProphNet: Efficient agent-centric motion forecasting with anchor-informed proposals. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02106"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Wang, J., Li, Y.H., and Huang, Y.K. (2023, January 17\u201324). Query-centric trajectory prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01713"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gu, J., Sun, C., and Zhao, H. (2021, January 11\u201317). Densetnt: End-to-end trajectory prediction from dense goal sets. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, J., Fang, L., Jiang, Q., and Zhou, B. (2021, January 19\u201325). Multimodal motion prediction with stacked transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00749"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., and Moutarde, F. (2021, January 19\u201322). Home: Heatmap output for future motion estimation. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564944"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., and Moutarde, F. (2022, January 23\u201327). Gohome: Graph-oriented heatmap output for future motion estimation. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812253"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ye, M., Cao, T., and Chen, Q. (2021, January 19\u201325). Tpcn: Temporal point cloud networks for motion forecasting. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01116"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Phan-Minh, T., Grigore, E.C., Boulton, F.A., Beijbom, O., and Wolff, E.M. (2020, January 14\u201319). Covernet: Multimodal behavior prediction using trajectory sets. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01408"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., and Pavone, M. (2020, January 23\u201328). Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XVIII 16.","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2716","DOI":"10.1109\/LRA.2022.3145090","article-title":"Stepwise goal-driven networks for trajectory prediction","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Luo, C., Sun, L., Dabiri, D., and Yuille, A. (2020, January 25\u201329). Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341034"},{"key":"ref_47","unstructured":"Deo, N., and Trivedi, M.M. (2020). Trajectory forecasts in unknown environments conditioned on grid-based plans. arXiv."},{"key":"ref_48","unstructured":"Deo, N., Wolff, E., and Beijbom, O. (2022, January 14\u201318). Multimodal trajectory prediction conditioned on lane-graph traversals. Proceedings of the Conference on Robot Learning, Auckland, New Zealand."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2065\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:17:44Z","timestamp":1760105864000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,23]]},"references-count":48,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072065"],"URL":"https:\/\/doi.org\/10.3390\/s24072065","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,23]]}}}