{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:31:52Z","timestamp":1784644312979,"version":"3.55.0"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585228","type":"print"},{"value":"9783030585235","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58523-5_40","type":"book-chapter","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T20:13:16Z","timestamp":1607026396000},"page":"683-700","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":853,"title":["Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data"],"prefix":"10.1007","author":[{"given":"Tim","family":"Salzmann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boris","family":"Ivanovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Punarjay","family":"Chakravarty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Pavone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.110"},{"key":"40_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations (2015)"},{"key":"40_CR3","unstructured":"Battaglia, P.W., Pascanu, R., Lai, M., Rezende, D., Kavukcuoglu, K.: Interaction networks for learning about objects, relations and physics. In: Conference on Neural Information Processing Systems (2016)"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (2015)","DOI":"10.18653\/v1\/K16-1002"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Britz, D., Goldie, A., Luong, M.T., Le, Q.V.: Massive exploration of neural machine translation architectures. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1442\u20131451 (2017)","DOI":"10.18653\/v1\/D17-1151"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving (2019)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"40_CR7","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 (2019)","DOI":"10.1109\/ICRA40945.2020.9196697"},{"key":"40_CR8","unstructured":"Casas, S., Luo, W., Urtasun, R.: IntentNet: learning to predict intention from raw sensor data. In: Conference on Robot Learning, pp. 947\u2013956 (2018)"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Chang, M.F., et al.: Argoverse: 3D tracking and forecasting with rich maps. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00895"},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1724\u20131734 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Deo, M.F., Trivedi, J.: Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs. In: IEEE Intelligent Vehicles Symposium (2018)","DOI":"10.1109\/IVS.2018.8500493"},{"key":"40_CR12","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Conference on Neural Information Processing Systems (2014)"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Li, F., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"40_CR14","doi-asserted-by":"publisher","unstructured":"Gweon, H., Saxe, R.: Developmental cognitive neuroscience of theory of mind, chap. 20. In: Neural Circuit Development and Function in the Brain, pp. 367\u2013377. Academic Press (2013). https:\/\/doi.org\/10.1016\/B978-0-12-397267-5.00057-1. http:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780123972675000571","DOI":"10.1016\/B978-0-12-397267-5.00057-1"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Hallac, D., Leskovec, J., Boyd, S.: Network lasso: clustering and optimization in large graphs. In: ACM International Conference on Knowledge Discovery and Data Mining (2015)","DOI":"10.1145\/2783258.2783313"},{"issue":"5","key":"40_CR16","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1103\/PhysRevE.51.4282","volume":"51","author":"D Helbing","year":"1995","unstructured":"Helbing, D., Moln\u00e1r, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282\u20134286 (1995)","journal-title":"Phys. Rev. E"},{"key":"40_CR17","unstructured":"Higgins, I., et al.: $$\\upbeta $$-VAE: learning basic visual concepts with a constrained variational framework. In: International Conference on Learning Representations (2017)"},{"key":"40_CR18","unstructured":"Ho, J., Ermon, S.: Multiple futures prediction. In: Conference on Neural Information Processing Systems (2019)"},{"key":"40_CR19","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Ivanovic, B., Pavone, M.: The trajectron: probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs. In: IEEE International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00246"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Ivanovic, B., Schmerling, E., Leung, K., Pavone, M.: Generative modeling of multimodal multi-human behavior. In: IEEE\/RSJ International Conference on Intelligent Robots & Systems (2018)","DOI":"10.1109\/IROS.2018.8594393"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-RNN: deep learning on spatio-temporal graphs. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.573"},{"key":"40_CR23","unstructured":"Jain, A., et al.: Discrete residual flow for probabilistic pedestrian behavior prediction. In: Conference on Robot Learning (2019)"},{"key":"40_CR24","unstructured":"Jang, E., Gu, S., Poole, B.: Categorial reparameterization with Gumbel-Softmax. In: International Conference on Learning Representations (2017)"},{"key":"40_CR25","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1115\/1.3662552","volume":"82","author":"RE Kalman","year":"1960","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems. ASME J. Basic Eng. 82, 35\u201345 (1960)","journal-title":"ASME J. Basic Eng."},{"key":"40_CR26","unstructured":"Kesten, R., et al.: Lyft Level 5 AV Dataset 2019 (2019). https:\/\/level5.lyft.com\/dataset\/"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Kong, J., Pfeifer, M., Schildbach, G., Borrelli, F.: Kinematic and dynamic vehicle models for autonomous driving control design. In: IEEE Intelligent Vehicles Symposium (2015)","DOI":"10.1109\/IVS.2015.7225830"},{"key":"40_CR28","unstructured":"Kosaraju, V., et al.: Social-BiGAT: multimodal trajectory forecasting using bicycle-GAN and graph attention networks. In: Conference on Neural Information Processing Systems (2019)"},{"key":"40_CR29","doi-asserted-by":"crossref","unstructured":"LaValle, S.M.: Better unicycle models. In: Planning Algorithms, p. 743. Cambridge University Press (2006)","DOI":"10.1017\/CBO9780511546877"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"LaValle, S.M.: A simple unicycle. In: Planning Algorithms, pp. 729\u2013730. Cambridge University Press (2006)","DOI":"10.1017\/CBO9780511546877"},{"key":"40_CR31","doi-asserted-by":"crossref","unstructured":"Lee, N., et al.: DESIRE: distant future prediction in dynamic scenes with interacting agents. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.233"},{"key":"40_CR32","doi-asserted-by":"crossref","unstructured":"Lee, N., Kitani, K.M.: Predicting wide receiver trajectories in American football. In: IEEE Winter Conference on Applications of Computer Vision (2016)","DOI":"10.1109\/WACV.2016.7477732"},{"issue":"3","key":"40_CR33","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1111\/j.1467-8659.2007.01089.x","volume":"26","author":"A Lerner","year":"2007","unstructured":"Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. Comput. Graph. Forum 26(3), 655\u2013664 (2007)","journal-title":"Comput. Graph. Forum"},{"issue":"5","key":"40_CR34","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TITS.2016.2603007","volume":"18","author":"J Morton","year":"2017","unstructured":"Morton, J., Wheeler, T.A., Kochenderfer, M.J.: Analysis of recurrent neural networks for probabilistic modeling of driver behavior. IEEE Trans. Pattern Anal. Mach. Intell. 18(5), 1289\u20131298 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"40_CR35","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/TIV.2016.2578706","volume":"1","author":"B Paden","year":"2016","unstructured":"Paden, B., \u010c\u00e1p, M., Yong, S.Z., Yershov, D., Frazzoli, E.: A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Veh. 1(1), 33\u201355 (2016)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"40_CR36","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: Conference on Neural Information Processing Systems - Autodiff Workshop (2017)"},{"key":"40_CR37","doi-asserted-by":"crossref","unstructured":"Pellegrini, S., Ess, A., Schindler, K., Gool, L.: You\u2019ll never walk alone: modeling social behavior for multi-target tracking. In: IEEE International Conference on Computer Vision (2009)","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"40_CR38","volume-title":"Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)","author":"CE Rasmussen","year":"2006","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), 1st edn. MIT Press, Cambridge (2006)","edition":"1"},{"key":"40_CR39","doi-asserted-by":"crossref","unstructured":"Rhinehart, N., McAllister, R., Kitani, K., Levine, S.: PRECOG: prediction conditioned on goals in visual multi-agent settings. In: IEEE International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00291"},{"key":"40_CR40","unstructured":"Rudenko, A., Palmieri, L., Herman, M., Kitani, K.M., Gavrila, D.M., Arras, K.O.: Human motion trajectory prediction: a survey (2019). https:\/\/arxiv.org\/abs\/1905.06113"},{"key":"40_CR41","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, S.H., Savarese, S.: SoPhie: an attentive GAN for predicting paths compliant to social and physical constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"40_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_10","volume-title":"Computer Vision - ECCV 2018","author":"A Sadeghian","year":"2018","unstructured":"Sadeghian, A., Legros, F., Voisin, M., Vesel, R., Alahi, A., Savarese, S.: CAR-Net: Clairvoyant attentive recurrent network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_10"},{"key":"40_CR43","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1109\/LRA.2020.2969925","volume":"5","author":"C Sch\u00f6ller","year":"2020","unstructured":"Sch\u00f6ller, C., Aravantinos, V., Lay, F., Knoll, A.: What the constant velocity model can teach us about pedestrian motion prediction. IEEE Robot. Autom. Lett. 5, 1696\u20131703 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"40_CR44","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: Conference on Neural Information Processing Systems (2015)"},{"key":"40_CR45","doi-asserted-by":"crossref","unstructured":"Thiede, L.A., Brahma, P.P.: Analyzing the variety loss in the context of probabilistic trajectory prediction. In: IEEE International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.01005"},{"key":"40_CR46","unstructured":"Thrun, S., Burgard, W., Fox, D.: The extended Kalman filter. In: Probabilistic Robotics, pp. 54\u201364. MIT Press (2005)"},{"key":"40_CR47","doi-asserted-by":"crossref","unstructured":"Vemula, A., Muelling, K., Oh, J.: Social attention: modeling attention in human crowds. In: Proceedings of the IEEE Conference on Robotics and Automation (2018)","DOI":"10.1109\/ICRA.2018.8460504"},{"issue":"2","key":"40_CR48","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1109\/TPAMI.2007.1167","volume":"30","author":"JM Wang","year":"2008","unstructured":"Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian process dynamical models for human motion. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 283\u2013298 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR49","unstructured":"Waymo: Safety report (2018). https:\/\/waymo.com\/safety\/. Accessed 9 Nov 2019"},{"key":"40_CR50","unstructured":"Waymo: Waymo Open Dataset: An autonomous driving dataset (2019). https:\/\/waymo.com\/open\/"},{"key":"40_CR51","doi-asserted-by":"crossref","unstructured":"Zeng, W., et al.: End-to-end interpretable neural motion planner. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00886"},{"key":"40_CR52","doi-asserted-by":"crossref","unstructured":"Zhao, S., Song, J., Ermon, S.: InfoVAE: balancing learning and inference in variational autoencoders. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)","DOI":"10.1609\/aaai.v33i01.33015885"},{"key":"40_CR53","doi-asserted-by":"crossref","unstructured":"Zhao, T., et al.: Multi-agent tensor fusion for contextual trajectory prediction. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.01240"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58523-5_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:13:50Z","timestamp":1733184830000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58523-5_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585228","9783030585235"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58523-5_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 December 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}