{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:24:51Z","timestamp":1783437891996,"version":"3.54.6"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732539","type":"print"},{"value":"9783031732546","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"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\/978-3-031-73254-6_7","type":"book-chapter","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T07:22:42Z","timestamp":1732692162000},"page":"106-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["UniTraj: A Unified Framework for\u00a0Scalable Vehicle Trajectory Prediction"],"prefix":"10.1007","author":[{"given":"Lan","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammadhossein","family":"Bahari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaouther Messaoud Ben","family":"Amor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c9loi","family":"Zablocki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthieu","family":"Cord","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandre","family":"Alahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Amirian, J., Zhang, B., Castro, F.V., Baldelomar, J.J., Hayet, J.B., Pettr\u00e9, J.: OpenTraj: assessing prediction complexity in human trajectories datasets. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69544-6_34"},{"key":"7_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103010","volume":"128","author":"M Bahari","year":"2021","unstructured":"Bahari, M., Nejjar, I., Alahi, A.: Injecting knowledge in data-driven vehicle trajectory predictors. Transp. Res. Part C: Emerg. Technol. 128, 103010 (2021)","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Bahari, M., et al.: Vehicle trajectory prediction works, but not everywhere. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17123\u201317133 (2022)","DOI":"10.1109\/CVPR52688.2022.01661"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Ben-Younes, H., Zablocki, \u00c9., Chen, M., P\u00e9rez, P., Cord, M.: Raising context awareness in motion forecasting. In: CVPRW, pp. 4408\u20134417. IEEE (2022)","DOI":"10.1109\/CVPRW56347.2022.00487"},{"key":"7_CR5","unstructured":"Bhattacharyya, P., Huang, C., Czarnecki, K.: SSL-lanes: self-supervised learning for motion forecasting in autonomous driving. In: Conference on Robot Learning, pp. 1793\u20131805. PMLR (2023)"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L., Eckstein, L.: The inD dataset: a drone dataset of naturalistic road user trajectories at German intersections. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 1929\u20131934. IEEE (2020)","DOI":"10.1109\/IV47402.2020.9304839"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621\u201311631 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"7_CR8","unstructured":"Caesar, H., et al.: nuPlan: a closed-loop ml-based planning benchmark for autonomous vehicles. arXiv preprint arXiv:2106.11810 (2021)"},{"key":"7_CR9","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-031-20065-6_3","volume-title":"ECCV 2022","author":"Y Cao","year":"2022","unstructured":"Cao, Y., Xiao, C., Anandkumar, A., Xu, D., Pavone, M.: AdvDO: realistic adversarial attacks for trajectory prediction. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13665, pp. 36\u201352. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20065-6_3"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Chang, M.-F., et\u00a0al.: Argoverse: 3D tracking and forecasting with rich maps. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00895"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Chen, G., Li, J., Lu, J., Zhou, J.: Human trajectory prediction via counterfactual analysis. In: ICCV, pp. 9804\u20139813. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.00968"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Chen, J., Wang, Z., Wang, J., Cai, B.: Q-eanet: implicit social modeling for trajectory prediction via experience-anchored queries. IET Intell. Transp. Syst. (2023)","DOI":"10.1049\/itr2.12477"},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.imavis.2017.11.006","volume":"69","author":"P Coscia","year":"2018","unstructured":"Coscia, P., Castaldo, F., Palmieri, F.A.N., Alahi, A., Savarese, S., Ballan, L.: Long-term path prediction in urban scenarios using circular distributions. J. Image Vis. Comput. 69, 81\u201391 (2018)","journal-title":"J. Image Vis. Comput."},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Ettinger, S., et\u00a0al.: Large scale interactive motion forecasting for autonomous driving: the waymo open motion dataset. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9710\u20139719 (2021)","DOI":"10.1109\/ICCV48922.2021.00957"},{"key":"7_CR15","unstructured":"Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., Moutarde, F.: Uncertainty estimation for cross-dataset performance in trajectory prediction. In: IEEE International Conference on Robotics and Automation Workshop on Fresh Perspectives on the Future of Autonomous Driving (2022)"},{"key":"7_CR16","unstructured":"Girgis, R., et al.: Latent variable sequential set transformers for joint multi-agent motion prediction. In: International Conference on Learning Representations (2022)"},{"key":"7_CR17","unstructured":"Houston, J., et al.: One thousand and one hours: self-driving motion prediction dataset. In: Conference on Robot Learning, pp. 409\u2013418. PMLR (2021)"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Hsu, K.C., Leung, K., Chen, Y., Fisac, J.F., Pavone, M.: Interpretable trajectory prediction for autonomous vehicles viacounterfactual responsibility. In: IEEE\/RSJ International Conference on Intelligent Robots & Systems (2023)","DOI":"10.1109\/IROS55552.2023.10341712"},{"key":"7_CR19","unstructured":"Ivanovic, B., Song, G., Gilitschenski, I., Pavone, M.: Trajdata: a unified interface to multiple human trajectory datasets. In: Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, New Orleans, USA (2023)"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Jiang, C., et\u00a0al.: Motiondiffuser: controllable multi-agent motion prediction using diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9644\u20139653 (2023)","DOI":"10.1109\/CVPR52729.2023.00930"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems (1960)","DOI":"10.1115\/1.3662552"},{"key":"7_CR22","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-031-19815-1_2","volume-title":"ECCV 2022","author":"D Kim","year":"2022","unstructured":"Kim, D., et al.: Learning semantic segmentation from multiple datasets with label shifts. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13688, pp. 20\u201336. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19815-1_2"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Kothari, P., Kreiss, S., Alahi, A.: Human trajectory forecasting in crowds: a deep learning perspective. IEEE Trans. Intell. Transp. Syst. (2021)","DOI":"10.1109\/TITS.2021.3069362"},{"key":"7_CR24","unstructured":"Kothari, P., Li, D., Liu, Y., Alahi, A.: Motion style transfer: modular low-rank adaptation for deep motion forecasting. In: CoRL. Proceedings of Machine Learning Research, vol. 205, pp. 774\u2013784. PMLR (2022)"},{"key":"7_CR25","unstructured":"Li, Q., et al.: Scenarionet: open-source platform for large-scale traffic scenario simulation and modeling. In: Advances in Neural Information Processing Systems (2023)"},{"key":"7_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/978-3-030-58536-5_32","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Liang","year":"2020","unstructured":"Liang, M., et al.: Learning lane graph representations for motion forecasting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 541\u2013556. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_32"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cadei, R., Schweizer, J., Bahmani, S., Alahi, A.: Towards robust and adaptive motion forecasting: a causal representation perspective. In: CVPR, pp. 17060\u201317071. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.01657"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Makansi, O., \u00c7i\u00e7ek, \u00d6., Marrakchi, Y., Brox, T.: On exposing the challenging long tail in future prediction of traffic actors. In: ICCV, pp. 13127\u201313137. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.01290"},{"key":"7_CR29","unstructured":"Malinin, A., et\u00a0al.: Shifts: a dataset of real distributional shift across multiple large-scale tasks. arXiv preprint arXiv:2107.07455 (2021)"},{"issue":"1","key":"7_CR30","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/TIV.2020.2991952","volume":"6","author":"K Messaoud","year":"2021","unstructured":"Messaoud, K., Yahiaoui, I., Verroust-Blondet, A., Nashashibi, F.: Attention based vehicle trajectory prediction. IEEE Trans. Intell. Veh. 6(1), 175\u2013185 (2021)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"7_CR31","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: IEEE International Conference on Robotics and Automation (ICRA), pp. 2980\u20132987. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10160609"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"Pourkeshavarz, M., Chen, C., Rasouli, A.: Learn tarot with mentor: a meta-learned self-supervised approach for trajectory prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8384\u20138393 (2023)","DOI":"10.1109\/ICCV51070.2023.00770"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: human trajectory prediction in crowded scenes. In: European Conference on Computer Vision (ECCV), vol.\u00a02, p.\u00a05 (2016)","DOI":"10.1007\/978-3-319-46484-8_33"},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Rudenko, A., Palmieri, L., Huang, W., Lilienthal, A.J., Arras, K.O.: The atlas benchmark: an automated evaluation framework for human motion prediction. In: 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 636\u2013643. IEEE (2022)","DOI":"10.1109\/RO-MAN53752.2022.9900656"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Saadatnejad, S., Bahari, M., Khorsandi, P., Saneian, M., Moosavi-Dezfooli, S.M., Alahi, A.: Are socially-aware trajectory prediction models really socially-aware? arXiv preprint arXiv:2108.10879 (2021)","DOI":"10.1016\/j.trc.2022.103705"},{"issue":"5","key":"7_CR36","doi-asserted-by":"publisher","first-page":"4447","DOI":"10.1109\/LRA.2024.3374188","volume":"9","author":"S Saadatnejad","year":"2024","unstructured":"Saadatnejad, S., et al.: Toward reliable human pose forecasting with uncertainty. IEEE Robot. Autom. Lett. 9(5), 4447\u20134454 (2024)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"7_CR37","unstructured":"Sadeghian, A., Kosaraju, V., Gupta, A., Savarese, S., Alahi, A.: Towards a benchmark for human trajectory prediction. arXiv preprint, Trajnet (2018)"},{"key":"7_CR38","unstructured":"Sarva, J., Wang, J., Tu, J., Xiong, Y., Manivasagam, S., Urtasun, R.: Adv3D: generating safety-critical 3D objects through closed-loop simulation. CoRR, abs\/2311.01446 (2023)"},{"key":"7_CR39","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer, M., Zhao, K., Kummert, A.: Caspnet++: joint multi-agent motion prediction (2023)","DOI":"10.1109\/IV55156.2024.10588873"},{"key":"7_CR40","doi-asserted-by":"crossref","unstructured":"Shao, W., Xu, Y., Li, J., Lv, C., Wang, W., Wang, H.: How does traffic environment quantitatively affect the autonomous driving prediction? IEEE Trans. Intell. Transp. Syst. (2023)","DOI":"10.1109\/TITS.2023.3278695"},{"key":"7_CR41","unstructured":"Shi, B., et al.: Multi-dataset pretraining: a unified model for semantic segmentation. arXiv preprint arXiv:2106.04121 (2021)"},{"key":"7_CR42","unstructured":"Shi, S., Jiang, L., Dai, D., Schiele, B.: Motion transformer with global intention localization and local movement refinement. In: Advances in Neural Information Processing Systems, vol. 35, pp. 6531\u20136543 (2022)"},{"key":"7_CR43","doi-asserted-by":"crossref","unstructured":"Varadarajan, B., et\u00a0al.: Multipath++: efficient information fusion and trajectory aggregation for behavior prediction. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 7814\u20137821. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9812107"},{"key":"7_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, P., Bai, L., Xue, J.: FEND: a future enhanced distribution-aware contrastive learning framework for long-tail trajectory prediction. In: CVPR, pp. 1400\u20131409. IEEE (2023)","DOI":"10.1109\/CVPR52729.2023.00141"},{"key":"7_CR45","doi-asserted-by":"crossref","unstructured":"Weng, X., Ivanovic, B., Kitani, K., Pavone, M.: Whose track is it anyway? Improving robustness to tracking errors with affinity-based trajectory prediction. In: CVPR, pp. 6563\u20136572. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.00646"},{"key":"7_CR46","unstructured":"Wilson, B., et\u00a0al. Argoverse 2: next generation datasets for self-driving perception and forecasting. arXiv preprint arXiv:2301.00493 (2023)"},{"key":"7_CR47","doi-asserted-by":"crossref","unstructured":"Xu, Y., et al.: Towards motion forecasting with real-world perception inputs: are end-to-end approaches competitive? In: ICRA (2024)","DOI":"10.1109\/ICRA57147.2024.10610201"},{"key":"7_CR48","unstructured":"Yao, Z., Li, X., Lang, B., Chuah, M.C.: Goal-LBP: goal-based local behavior guided trajectory prediction for autonomous driving. IEEE Trans. Intell. Transp. Syst. 1\u201310 (2023)"},{"key":"7_CR49","doi-asserted-by":"crossref","unstructured":"Ye, L., Zhou, Z., Wang, J.: Improving the generalizability of trajectory prediction models with frenet-based domain normalization. In: IEEE International Conference on Robotics and Automation (ICRA) (2023)","DOI":"10.1109\/ICRA48891.2023.10160788"},{"key":"7_CR50","unstructured":"Zhan, W., et\u00a0al.: Interaction dataset: an international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps. arXiv preprint arXiv:1910.03088 (2019)"},{"key":"7_CR51","unstructured":"Zhang, P., Bai, L., Xue, J., Fang, J., Zheng, N., Ouyang, W.: Trajectory forecasting from detection with uncertainty-aware motion encoding. CoRR, abs\/2202.01478 (2022)"},{"key":"7_CR52","doi-asserted-by":"crossref","unstructured":"Zhou, X., Koltun, V., Kr\u00e4henb\u00fchl, P.: Simple multi-dataset detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7571\u20137580 (2022)","DOI":"10.1109\/CVPR52688.2022.00742"},{"key":"7_CR53","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Wang, J., Li, Y.-H., Huang, Y.-K.: Query-centric trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17863\u201317873 (2023)","DOI":"10.1109\/CVPR52729.2023.01713"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73254-6_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T08:04:26Z","timestamp":1732694666000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73254-6_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,28]]},"ISBN":["9783031732539","9783031732546"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73254-6_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,28]]},"assertion":[{"value":"28 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}