{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T03:07:35Z","timestamp":1768532855842,"version":"3.49.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2023YFB2504400"],"award-info":[{"award-number":["2023YFB2504400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the youth science fund projects granted by the National Natural Science Foundation of China","award":["52102463"],"award-info":[{"award-number":["52102463"]}]},{"name":"Taishan Industrial Experts Program","award":["tscx202211119"],"award-info":[{"award-number":["tscx202211119"]}]},{"name":"Taishan Industrial Experts Program","award":["tscx202211119"],"award-info":[{"award-number":["tscx202211119"]}]},{"name":"Taishan Industrial Experts Program","award":["tscx202211119"],"award-info":[{"award-number":["tscx202211119"]}]},{"name":"Taishan Industrial Experts Program","award":["tscx202211119"],"award-info":[{"award-number":["tscx202211119"]}]},{"name":"Taishan Industrial Experts Program","award":["tscx202211119"],"award-info":[{"award-number":["tscx202211119"]}]},{"name":"Taishan Industrial Experts Program","award":["tscx202211119"],"award-info":[{"award-number":["tscx202211119"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-01111-z","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T18:45:05Z","timestamp":1765997105000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ACDD: Multi-traffic Participant Interactive Motion Prediction with Agent -Centric Scene Modeling and Dual-Layer Decoding"],"prefix":"10.1007","volume":"19","author":[{"given":"Ke","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Honglin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siju","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulong","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"issue":"3","key":"1111_CR1","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/87.845881","volume":"8","author":"CF Lin","year":"2000","unstructured":"Lin, C.F., Ulsoy, A.G., LeBlanc, D.J.: Vehicle dynamics and external disturbance estimation for vehicle path prediction. IEEE Trans. Control Syst. Technol. 8(3), 508\u2013518 (2000)","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"1111_CR2","doi-asserted-by":"crossref","unstructured":"Ammoun, S., Nashashibi, F.: Real time trajectory prediction for collision risk estimation between vehicles. In: IEEE 5th international conference on intelligent computer communication and processing, pp. 417\u2013422, IEEE(2009)","DOI":"10.1109\/ICCP.2009.5284727"},{"issue":"4","key":"1111_CR3","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1109\/TITS.2011.2157342","volume":"12","author":"M Althoff","year":"2011","unstructured":"Althoff, M., Mergel, A.: Comparison of markov chain abstraction and monte carlo simulation for the safety assessment of autonomous cars. IEEE Trans. Intell. Transp. Syst. 12(4), 1237\u20131247 (2011)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1111_CR4","doi-asserted-by":"crossref","unstructured":"Kaempchen, N., Weiss, K., Dietmayer, K.C.: Imm object tracking for high dynamic driving maneuvers. IEEE Intelligent Vehicles Symposium, pp. 825\u2013830, (2004)","DOI":"10.1109\/IVS.2004.1336491"},{"key":"1111_CR5","doi-asserted-by":"crossref","unstructured":"Guo, Y., Kalidindi, V.V., Arief, M., Wang, W., Zhu, J., Peng, H., Zhao, D.: Modeling multi-vehicle interaction scenes using gaussian random field. In: IEEE Intelligent Transportation Systems Conference, pp. 3974\u20133980, IEEE (2019)","DOI":"10.1109\/ITSC.2019.8917516"},{"key":"1111_CR6","doi-asserted-by":"publisher","first-page":"30210","DOI":"10.1109\/ACCESS.2020.2971705","volume":"12","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Zhi, Y., He, R., Li, J.: Research on traffic vehicle behavior prediction method based on game theory and hmm. IEEE Access 12, 30210\u201330222 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"1111_CR7","first-page":"69","volume":"7","author":"T Gindele","year":"2015","unstructured":"Gindele, T., Brechtel, S., Dillmann, R.: Learning driver behavior models from traffic observations for decision making and planning. IEEE Intell. Transp. Syst. Mag. 7(1), 69\u201379 (2015)","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"issue":"4","key":"1111_CR8","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s10514-011-9248-x","volume":"31","author":"J Joseph","year":"2011","unstructured":"Joseph, J., Doshi-Velez, F., Huang, A.S., Roy, N.: A bayesian nonparametric approach to modeling motion patterns. Auton. Robot. 31(4), 383\u2013400 (2011)","journal-title":"Auton. Robot."},{"key":"1111_CR9","doi-asserted-by":"publisher","first-page":"38287","DOI":"10.1109\/ACCESS.2019.2907000","volume":"7","author":"S Dai","year":"2019","unstructured":"Dai, S., Li, L., Li, Z.: Modeling vehicle interactions via modified lstm models for trajectory prediction. IEEE Access 7, 38287\u201338296 (2019)","journal-title":"IEEE Access"},{"key":"1111_CR10","unstructured":"Chai, Y., Sapp, B., Bansal, M., Anguelov, D.: Multipath: multiple probabilistic anchor trajectory hypotheses for behavior prediction. arXiv:1910.05449. (2019)"},{"key":"1111_CR11","doi-asserted-by":"crossref","unstructured":"Djuric, N., Radosavljevic, V., Cui, H., Nguyen, T., Chou, F.C., Lin, T.H., Schneider, J.: Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving. In: IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2095\u20132104, (2020)","DOI":"10.1109\/WACV45572.2020.9093332"},{"key":"1111_CR12","unstructured":"Zhang, Y., Zou, Y., Tang, J.: A lane-changing prediction method based on temporal convolution network. arXiv:2011.01224. (2020)"},{"key":"1111_CR13","doi-asserted-by":"crossref","unstructured":"Zeng, W., Liang, M., Liao, R., Urtasun, R.: Lanercnn: Distributed representations for graph-centric motion forecasting. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 532\u2013539, IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9636035"},{"key":"1111_CR14","doi-asserted-by":"crossref","unstructured":"Gao, J., Sun, C., Zhao, H., Shen, Y., Anguelov, D., Li, C., Schmid, C.: Vectornet: Encoding hd maps and agent dynamics from vectorized representation. In: IEEE\/CVF conference on computer vision and pattern recognition, pp. 11525\u201311533, (2020)","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"1111_CR15","doi-asserted-by":"crossref","unstructured":"Gu, J., Sun, C., Zhao, H.: Densetnt: End-to-end trajectory prediction from dense goal sets. In: IEEE\/CVF conference on computer vision and pattern recognition, pp. 15303\u201315312, (2021)","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"1111_CR16","doi-asserted-by":"crossref","unstructured":"Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., Moutarde, F.: Home: Heatmap output for future motion estimation. In: IEEE International Intelligent Transportation Systems Conference, pp. 500\u2013507, IEEE (2021)","DOI":"10.1109\/ITSC48978.2021.9564944"},{"key":"1111_CR17","doi-asserted-by":"crossref","unstructured":"Cheng, J., Mei, X., Liu, M.: Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders. In: IEEE\/CVF International Conference on Computer Vision, pp. 8679\u20138689, (2023)","DOI":"10.1109\/ICCV51070.2023.00797"},{"key":"1111_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Wang, J.Y.H., Huang, Y.K.: Query-centric trajectory prediction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17863\u201317873, (2023)","DOI":"10.1109\/CVPR52729.2023.01713"},{"key":"1111_CR19","doi-asserted-by":"crossref","unstructured":"Casas, S., Gulino, C., Liao, R., Urtasun, R.: Spagnn: Spatially-aware graph neural networks for relational behavior forecasting from sensor data. In: IEEE International Conference on Robotics and Automation, pp. 9491\u20139497, IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9196697"},{"key":"1111_CR20","doi-asserted-by":"crossref","unstructured":"Casas, S., Sadat, A., Urtasun, R.: Mp3: A unified model to map, perceive, predict and plan. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14403\u201314412, (2021)","DOI":"10.1109\/CVPR46437.2021.01417"},{"key":"1111_CR21","doi-asserted-by":"crossref","unstructured":"Marchetti, F., Becattini, F., Seidenari, L., Bimbo, A.D.: Mantra: Memory augmented networks for multiple trajectory prediction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7143\u20137152, (2020)","DOI":"10.1109\/CVPR42600.2020.00717"},{"key":"1111_CR22","unstructured":"Casas, S., Luo, W., Urtasun, R.: Intentnet: Learning to predict intention from raw sensor data. In: 35th International Conference on Machine Learning, pp. 947\u2013956, PMLR (2018)"},{"key":"1111_CR23","doi-asserted-by":"crossref","unstructured":"Ye, M., Cao, T., Chen, Q.: Tpcn: Temporal point cloud networks for motion forecasting. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11318\u201311327, (2021)","DOI":"10.1109\/CVPR46437.2021.01116"},{"key":"1111_CR24","unstructured":"Zhao, H., Gao, J., Lan, T., Sun, C., Sapp, B., Varadarajan, B.: Tnt: Target-driven trajectory prediction. In: 35th International Conference on Machine Learning, pp. 895\u2013904, PMLR (2021)"},{"key":"1111_CR25","doi-asserted-by":"crossref","unstructured":"Liang, M., Yang, B., Hu, R., Chen, Y., Liao, R., Feng, S., Urtasun, R.: Learning lane graph representations for motion forecasting. In: Computer Vision\u2013ECCV 2020: 16th European Conference, pp. 541\u2013556, Cham: Springer International Publishing (2020)","DOI":"10.1007\/978-3-030-58536-5_32"},{"key":"1111_CR26","doi-asserted-by":"crossref","unstructured":"Sun, Q., Huang, X., Gu, J., Williams, B.C., Zhao, H.: M2i: From factored marginal trajectory prediction to interactive prediction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6543\u20136552, (2022)","DOI":"10.1109\/CVPR52688.2022.00643"},{"key":"1111_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, J., Fang, L., Jiang, Q., Zhou, B.: Multimodal motion prediction with stacked transformers. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7577\u20137586, (2021)","DOI":"10.1109\/CVPR46437.2021.00749"},{"key":"1111_CR28","doi-asserted-by":"crossref","unstructured":"Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., Moutarde, F.: Gohome: Graph-oriented heatmap output for future motion estimation. In: IEEE International Conference on Robotics and Automation, pp. 9107\u20139114, IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9812253"},{"issue":"3","key":"1111_CR29","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TIV.2022.3167103","volume":"7","author":"Y Huang","year":"2022","unstructured":"Huang, Y., Du, J., Yang, Z., Zhou, Z., Zhang, L., Chen, H.: A survey on trajectory-prediction methods for autonomous driving. IEEE Trans. Intel. Veh. 7(3), 652\u2013674 (2022)","journal-title":"IEEE Trans. Intel. Veh."},{"key":"1111_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2024.103748","volume":"191","author":"X Mo","year":"2024","unstructured":"Mo, X., Xing, Y., Lv, C.: Heterogeneous graph social pooling for interaction-aware vehicle trajectory prediction. Transp.  Res. Part E: Logist. Transp. Rev. 191, 103748 (2024)","journal-title":"Transp. Res. Part E: Logist. Transp. Rev."},{"issue":"2","key":"1111_CR31","doi-asserted-by":"publisher","first-page":"3459","DOI":"10.1109\/LRA.2021.3062807","volume":"6","author":"X Li","year":"2021","unstructured":"Li, X., Rosman, G., Gilitschenski, I., Vasile, C.I., DeCastro, J.A., Karaman, S., Rus, D.: Vehicle trajectory prediction using generative adversarial network with temporal logic syntax tree features. IEEE Robot. Autom. Lett. 6(2), 3459\u20133466 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"1111_CR32","doi-asserted-by":"crossref","unstructured":"Li, X., Ying, X., Chuah, M.C.: Grip++. Enhanced graph-based interaction-aware trajectory prediction for autonomous driving. arXiv:1907.07792. (2019)","DOI":"10.1109\/ITSC.2019.8917228"},{"key":"1111_CR33","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, K., Li, H., Gao, Q., Wang, X.: Vehicle trajectory prediction using hierarchical lstm and graph attention network. IEEE Internet Things J. 12, 7010\u20137025 (2024)","DOI":"10.1109\/JIOT.2024.3493208"},{"key":"1111_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113363","volume":"316","author":"X Chen","year":"2025","unstructured":"Chen, X., Wang, J., Deng, F., Li, Z.: Adaptive graph transformer with future interaction modeling for multi-agent trajectory prediction. Knowledge-Based Syst. 316, 113363 (2025)","journal-title":"Knowledge-Based Syst."},{"key":"1111_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Ye, L., Wang, J., Wu, K., Lu, K.: Hivt: hierarchical vector transformer for multi-agent motion prediction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8823\u20138833, (2022)","DOI":"10.1109\/CVPR52688.2022.00862"},{"key":"1111_CR36","doi-asserted-by":"crossref","unstructured":"Woo, S., Kim, M., Kim, D., Jang, S., Lee, S.: Fimp: future interaction modeling for multi-agent motion prediction. In: IEEE International Conference on Robotics and Automation, pp. 14457\u201314463, (2024)","DOI":"10.1109\/ICRA57147.2024.10611080"},{"key":"1111_CR37","doi-asserted-by":"crossref","unstructured":"Varadarajan, B., Hefny, A., Srivastava, A., Refaat, K.S., Nayakanti, N., Cornman, A., Sapp, B.: Multipath++: efficient information fusion and trajectory aggregation for behavior prediction. In: IEEE International Conference on Robotics and Automation, pp. 7814\u20137821, (2022)","DOI":"10.1109\/ICRA46639.2022.9812107"},{"key":"1111_CR38","unstructured":"Ngiam, J., Caine, B., Vasudevan, V., Zhang, Z., Chiang, H.T.L., Ling, J., Shlens, J.: Scene transformer: a unified architecture for predicting multiple agent trajectories. arXiv:2106.08417. (2021)"},{"key":"1111_CR39","doi-asserted-by":"crossref","unstructured":"SSeff, A., Cera, B., Chen, D., Ng, M., Zhou, A., Nayakanti, N., Sapp, B.: Motionlm: multi-agent motion forecasting as language modeling. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8579\u20138590, (2023)","DOI":"10.1109\/ICCV51070.2023.00788"},{"key":"1111_CR40","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social gan: socially acceptable trajectories with generative adversarial networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2255\u20132264, (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"1111_CR41","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., Savarese, S.: Sophie: An attentive gan for predicting paths compliant to social and physical constraints. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1349\u20131358, (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"1111_CR42","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Weng, X., Ou, Y., Kitani, K.M.: Agentformer: agent-aware transformers for socio-temporal multi-agent forecasting. In: IEEE\/CVF international conference on computer vision, pp. 9813\u20139823, (2021)","DOI":"10.1109\/ICCV48922.2021.00967"},{"key":"1111_CR43","doi-asserted-by":"crossref","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data. In: Computer Vision\u2013ECCV 2020: 16th European Conference, pp. 683\u2013700, Cham: Springer International Publishing. (2020)","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"1111_CR44","doi-asserted-by":"crossref","unstructured":"Aydemir, G., Akan, A.K., G\u00fcney, F.: Adapt: efficient multi-agent trajectory prediction with adaptation. In: IEEE\/CVF international conference on computer vision, pp. 8295\u20138305, (2023)","DOI":"10.1109\/ICCV51070.2023.00762"},{"key":"1111_CR45","unstructured":"Lan, Z., Jiang, Y., Mu, Y., Chen, C., Li, S.E.: Sept: Towards efficient scene representation learning for motion prediction. arXiv:2309.15289. (2023)"},{"key":"1111_CR46","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhu, X., Yu, C., Li, W., Ma, Y., Jin, R., Yang, W.: Ganet: goal area network for motion forecasting. In: IEEE International Conference on Robotics and Automation, pp. 1609\u20131615, IEEE(2023)","DOI":"10.1109\/ICRA48891.2023.10160468"},{"key":"1111_CR47","unstructured":"Lee, C.L., Wang, Z.X., Lai, K.T., Fadillah, A.: Goalnet: Goal areas oriented pedestrian trajectory prediction. arXiv:2402.19002. (2024)"},{"issue":"5","key":"1111_CR48","doi-asserted-by":"publisher","first-page":"3955","DOI":"10.1109\/TPAMI.2024.3352811","volume":"46","author":"S Shi","year":"2024","unstructured":"Shi, S., Jiang, L., Dai, D., Schiele, B.: Mtr++: multi-agent motion prediction with symmetric scene modeling and guided intention querying. IEEE Trans. Pattern Anal. Mach. Intell. 46(5), 3955\u20133971 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"1111_CR49","doi-asserted-by":"publisher","first-page":"2532650","DOI":"10.1080\/21680566.2025.2532650","volume":"13","author":"W Chen","year":"2025","unstructured":"Chen, W., Sang, H., Zhao, Z.: Hogat: higher-order graph attention network for pedestrian trajectory prediction. Transportmetrica B-Transp. Dyn. 13(1), 2532650 (2025)","journal-title":"Transportmetrica B-Transp. Dyn."},{"issue":"1","key":"1111_CR50","doi-asserted-by":"publisher","first-page":"2389896","DOI":"10.1080\/21680566.2024.2389896","volume":"12","author":"W Chen","year":"2024","unstructured":"Chen, W., Sang, H., Wang, J., Zhao, Z.: Imgcn: interpretable masked graph convolution network for pedestrian trajectory prediction. Transportmetrica B-Transp. Dyn. 12(1), 2389896 (2024)","journal-title":"Transportmetrica B-Transp. Dyn."},{"issue":"13","key":"1111_CR51","doi-asserted-by":"publisher","first-page":"25033","DOI":"10.1109\/JIOT.2025.3556839","volume":"12","author":"W Chen","year":"2025","unstructured":"Chen, W., Sang, H., Zhao, Z.: Pchgcn: physically constrained higher-order graph convolutional network for pedestrian trajectory prediction. IEEE Internet Things J. 12(13), 25033\u201325045 (2025)","journal-title":"IEEE Internet Things J."},{"issue":"5","key":"1111_CR52","doi-asserted-by":"publisher","first-page":"6923","DOI":"10.1109\/TITS.2024.3525080","volume":"26","author":"W Chen","year":"2025","unstructured":"Chen, W., Sang, H., Wang, J., Zhao, Z.: Dstigcn: deformable spatial-temporal interaction graph convolution network for pedestrian trajectory prediction. IEEE Trans. Intell. Transp. Syst. 26(5), 6923\u20136935 (2025)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1111_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2024.104862","volume":"156","author":"W Chen","year":"2025","unstructured":"Chen, W., Sang, H., Wang, J., Zhao, Z.: Iggcn: individual-guided graph convolution network for pedestrian trajectory prediction. Digit. Signal Prog. 156, 104862 (2025)","journal-title":"Digit. Signal Prog."},{"issue":"2","key":"1111_CR54","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, M., Crandall, D.J.: Stepwise goal-driven networks for trajectory prediction. IEEE Robot. Autom. Lett. 7(2), 2716\u20132723 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"1111_CR55","doi-asserted-by":"crossref","unstructured":"Song, N., Zhang, B., Zhu, X., Zhang, L.: Motion forecasting in continuous driving. arXiv:2410.06007. (2024)","DOI":"10.52202\/079017-2484"},{"key":"1111_CR56","unstructured":"Zhang, B., Song, N., Zhang, L.: Decoupling motion forecasting into directional intentions and dynamic states. arXiv:2410.05982. (2024)"},{"key":"1111_CR57","unstructured":"Argoverse Team. Argoverse 2 motion forecasting competition: Leaderboard for multi-world forecasting challenge. (2024). Available online: https:\/\/eval.ai\/web\/challenges\/challenge-page\/1719\/leaderboard\/4761"},{"issue":"5","key":"1111_CR58","doi-asserted-by":"publisher","first-page":"2946","DOI":"10.1109\/LRA.2023.3262150","volume":"8","author":"X Gao","year":"2023","unstructured":"Gao, X., Jia, X., Li, Y., Xiong, H.: Dynamic scene representation learning for motion forecasting with heterogeneous graph convolutional recurrent networks. IEEE Robot. Autom. Lett. 8(5), 2946\u20132953 (2023)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"1111_CR59","doi-asserted-by":"crossref","unstructured":"Wang, X., Su, T., Da, F., Yang, X.: Prophnet: efficient agent-centric motion forecasting with anchor-informed proposals. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21995\u201322003, (2023)","DOI":"10.1109\/CVPR52729.2023.02106"},{"key":"1111_CR60","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Shao, H., Wang, L., Waslander, S.L., Li, H., Liu, Y.: Smartrefine: a scenario-adaptive refinement framework for efficient motion prediction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15281\u201315290, (2024)","DOI":"10.1109\/CVPR52733.2024.01447"},{"issue":"10","key":"1111_CR61","doi-asserted-by":"publisher","first-page":"6795","DOI":"10.1109\/LRA.2023.3311351","volume":"8","author":"C Feng","year":"2023","unstructured":"Feng, C., Zhou, H., Lin, H., Zhang, Z., Xu, Z., Zhang, C., Shen, S.: Macformer: map-agent coupled transformer for real-time and robust trajectory prediction. IEEE Robot. Autom. Lett. 8(10), 6795\u20136802 (2023)","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01111-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-01111-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01111-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T10:36:18Z","timestamp":1768473378000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-01111-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1111"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-01111-z","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"16 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2025","order":4,"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 no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical 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":"23"}}