{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:09:47Z","timestamp":1766138987671,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031783944"},{"type":"electronic","value":"9783031783951"}],"license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78395-1_13","type":"book-chapter","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T09:36:18Z","timestamp":1733132178000},"page":"186-201","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Long-Range Context Modeling for Motion Forecasting with State Space Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4198-8858","authenticated-orcid":false,"given":"Zhiwei","family":"Dong","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2004-5106","authenticated-orcid":false,"given":"Ran","family":"Ding","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3749-9028","authenticated-orcid":false,"given":"Jiaxiang","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2258-6325","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"13_CR2","unstructured":"Chai, Y., Sapp, B., Bansal, M., Anguelov, D.: Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. arXiv preprint arXiv:1910.05449 (2019)"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Cui, A., Casas, S., Wong, K., Suo, S., Urtasun, R.: Gorela: Go relative for viewpoint-invariant motion forecasting. In: 2023 IEEE International Conference on Robotics and Automation","DOI":"10.1109\/ICRA48891.2023.10160984"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Cui, H., Radosavljevic, V., Chou, F.C., Lin, T.H., Nguyen, T., Huang, T.K., Schneider, J., Djuric, N.: Multimodal trajectory predictions for autonomous driving using deep convolutional networks. In: 2019 international conference on robotics and automation","DOI":"10.1109\/ICRA.2019.8793868"},{"key":"13_CR5","unstructured":"Dao, T., Fu, D.Y., Saab, K.K., Thomas, A.W., Rudra, A., R\u00e9, C.: Hungry hungry hippos: Towards language modeling with state space models. In: Proceedings of the 11th International Conference on Learning Representations (ICLR) (2023)"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Dong, Z., Li, G., Liao, Y., Wang, F., Ren, P., Qian, C.: Centripetalnet: Pursuing high-quality keypoint pairs for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.01053"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Dong, Z., Zhu, X., Cao, X., Ding, R., Li, W., Zhou, C., Wang, Y., Liu, Q.: Bezierformer: A unified architecture for 2d and 3d lane detection. In: IEEE International Conference on Multimedia and Expo (2024)","DOI":"10.1109\/ICME57554.2024.10688364"},{"key":"13_CR8","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: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"13_CR9","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)"},{"key":"13_CR10","unstructured":"Gu, A., Goel, K., Gupta, A., R\u00e9, C.: On the parameterization and initialization of diagonal state space models. Advances in Neural Information Processing Systems (2022)"},{"key":"13_CR11","unstructured":"Gu, A., Goel, K., R\u00e9, C.: Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396 (2021)"},{"key":"13_CR12","unstructured":"Gu, A., Johnson, I., Goel, K., Saab, K., Dao, T., Rudra, A., R\u00e9, C.: Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in neural information processing systems (2021)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Gu, J., Sun, C., Zhao, H.: Densetnt: End-to-end trajectory prediction from dense goal sets. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., Xia, S.T.: Mambair: A simple baseline for image restoration with state-space model. arXiv preprint arXiv:2402.15648 (2024)","DOI":"10.1007\/978-3-031-72649-1_13"},{"key":"13_CR15","unstructured":"Gupta, A., Gu, A., Berant, J.: Diagonal state spaces are as effective as structured state spaces. Advances in Neural Information Processing Systems (2022)"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: ICCV (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Jia, X., Wu, P., Chen, L., Liu, Y., Li, H., Yan, J.: Hdgt: Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding. IEEE transactions on pattern analysis and machine intelligence (2023)","DOI":"10.1109\/TPAMI.2023.3298301"},{"key":"13_CR18","unstructured":"Lan, Z., Jiang, Y., Mu, Y., Chen, C., Li, S.E.: Sept: Towards efficient scene representation learning for motion prediction. In: International Conference on Learning Representations (ICLR) (2024)"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H., Chandraker, M.: Desire: Distant future prediction in dynamic scenes with interacting agents. In: ICCV (2017)","DOI":"10.1109\/CVPR.2017.233"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Li, K., Li, X., Wang, Y., He, Y., Wang, Y., Wang, L., Qiao, Y.: Videomamba: State space model for efficient video understanding. arXiv preprint arXiv:2403.06977 (2024)","DOI":"10.1007\/978-3-031-73347-5_14"},{"key":"13_CR21","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: ECCV (2020)","DOI":"10.1007\/978-3-030-58536-5_32"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, J., Fang, L., Jiang, Q., Zhou, B.: Multimodal motion prediction with stacked transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00749"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Marchetti, F., Becattini, F., Seidenari, L., Bimbo, A.D.: Mantra: Memory augmented networks for multiple trajectory prediction. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00717"},{"key":"13_CR24","unstructured":"Mehta, H., Gupta, A., Cutkosky, A., Neyshabur, B.: Long range language modeling via gated state spaces. In: International Conference on Learning Representations (ICLR) (2023)"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Nayakanti, N., Al-Rfou, R., Zhou, A., Goel, K., Refaat, K.S., Sapp, B.: Wayformer: Motion forecasting via simple & efficient attention networks. In: 2023 IEEE International Conference on Robotics and Automation","DOI":"10.1109\/ICRA48891.2023.10160609"},{"key":"13_CR26","unstructured":"Ngiam, J., Caine, B., Vasudevan, V., Zhang, Z., Chiang, H.T.L., Ling, J., Roelofs, R., Bewley, A., Liu, C., Venugopal, A., et\u00a0al.: Scene transformer: A unified architecture for predicting multiple agent trajectories. arXiv preprint arXiv:2106.08417 (2021)"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"13_CR28","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Varadarajan, B., Hefny, A., Srivastava, A., Refaat, K.S., Nayakanti, N., Cornman, A., Chen, K., Douillard, B., Lam, C.P., Anguelov, D., et\u00a0al.: Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction. In: 2022 International Conference on Robotics and Automation","DOI":"10.1109\/ICRA46639.2022.9812107"},{"key":"13_CR30","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems (2017)"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhu, X., Yu, C., Li, W., Ma, Y., Jin, R., Ren, X., Ren, D., Wang, M., Yang, W.: Ganet: Goal area network for motion forecasting. In: 2023 IEEE International Conference on Robotics and Automation","DOI":"10.1109\/ICRA48891.2023.10160468"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Wang, X., Su, T., Da, F., Yang, X.: Prophnet: Efficient agent-centric motion forecasting with anchor-informed proposals. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.02106"},{"key":"13_CR33","unstructured":"Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J., Khandelwal, S., Pan, B., Kumar, R., Hartnett, A., Pontes, J.K., et\u00a0al.: Argoverse 2: Next generation datasets for self-driving perception and forecasting. arXiv preprint arXiv:2301.00493 (2023)"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Yu, C., Ma, X., Ren, J., Zhao, H., Yi, S.: Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58610-2_30"},{"key":"13_CR35","unstructured":"Zhang, C., Sun, H., Chen, C., Guo, Y.: Banet: Motion forecasting with boundary aware network. arXiv preprint arXiv:2206.07934 (2022)"},{"key":"13_CR36","unstructured":"Zhang, Z., Liniger, A., Sakaridis, C., Yu, F., Gool, L.V.: Real-time motion prediction via heterogeneous polyline transformer with relative pose encoding. Advances in Neural Information Processing Systems (2024)"},{"key":"13_CR37","unstructured":"Zhao, H., Gao, J., Lan, T., Sun, C., Sapp, B., Varadarajan, B., Shen, Y., Shen, Y., Chai, Y., Schmid, C., et\u00a0al.: Tnt: Target-driven trajectory prediction. In: Conference on Robot Learning (2021)"},{"key":"13_CR38","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: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01447"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Wang, J., Li, Y.H., Huang, Y.K.: Query-centric trajectory prediction. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01713"},{"key":"13_CR40","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: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00862"},{"key":"13_CR41","unstructured":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., Wang, X.: Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417 (2024)"},{"key":"13_CR42","unstructured":"Zhu, X., Cao, X., Dong, Z., Zhou, C., Liu, Q., Li, W., Wang, Y.: Nemo: Neural map growing system for spatiotemporal fusion in bird\u2019s-eye-view and bdd-map benchmark. arXiv preprint arXiv:2306.04540 (2023)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78395-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T10:12:02Z","timestamp":1733134322000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78395-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"ISBN":["9783031783944","9783031783951"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78395-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"3 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}