{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:49:30Z","timestamp":1783608570426,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811692468","type":"print"},{"value":"9789811692475","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-9247-5_13","type":"book-chapter","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T21:25:33Z","timestamp":1641936333000},"page":"177-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Social-Transformer: Pedestrian Trajectory Prediction in Autonomous Driving Scenes"],"prefix":"10.1007","author":[{"given":"Hanbing","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuchun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"13_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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961\u2013971 (2016)","DOI":"10.1109\/CVPR.2016.110"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Alahi, A., Ramanathan, V., Fei-Fei, L.: Socially-aware large-scale crowd forecasting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2203\u20132210 (2014)","DOI":"10.1109\/CVPR.2014.283"},{"issue":"8","key":"13_CR3","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.trb.2005.09.006","volume":"40","author":"G Antonini","year":"2006","unstructured":"Antonini, G., Bierlaire, M., Weber, M.: Discrete choice models of pedestrian walking behavior. Transp. Res. Part B: Methodol. 40(8), 667\u2013687 (2006)","journal-title":"Transp. Res. Part B: Methodol."},{"key":"13_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"13_CR5","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"4","key":"13_CR6","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi, L., Chiriatti, M.: GPT-3: its nature, scope, limits, and consequences. Minds Mach. 30(4), 681\u2013694 (2020). https:\/\/doi.org\/10.1007\/s11023-020-09548-1","journal-title":"Minds Mach."},{"key":"13_CR7","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2255\u20132264 (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Hasan, I., Setti, F., Tsesmelis, T., Del Bue, A., Galasso, F., Cristani, M.: MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6067\u20136076 (2018)","DOI":"10.1109\/CVPR.2018.00635"},{"issue":"5","key":"13_CR9","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1103\/PhysRevE.51.4282","volume":"51","author":"D Helbing","year":"1995","unstructured":"Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)","journal-title":"Phys. Rev. E"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Huang, Y., Bi, H., Li, Z., Mao, T., Wang, Z.: STGAT: modeling spatial-temporal interactions for human trajectory prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6272\u20136281 (2019)","DOI":"10.1109\/ICCV.2019.00637"},{"key":"13_CR11","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"issue":"1","key":"13_CR12","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/TPAMI.2015.2430335","volume":"38","author":"HS Koppula","year":"2015","unstructured":"Koppula, H.S., Saxena, A.: Anticipating human activities using object affordances for reactive robotic response. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 14\u201329 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer Graphics Forum, vol. 26, pp. 655\u2013664. Wiley (2007)","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Liang, J., Jiang, L., Niebles, J.C., Hauptmann, A.G., Fei-Fei, L.: Peeking into the future: predicting future person activities and locations in videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5725\u20135734 (2019)","DOI":"10.1109\/CVPRW.2019.00358"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Luber, M., Stork, J.A., Tipaldi, G.D., Arras, K.O.: People tracking with human motion predictions from social forces. In: 2010 IEEE International Conference on Robotics and Automation, pp. 464\u2013469. IEEE (2010)","DOI":"10.1109\/ROBOT.2010.5509779"},{"key":"13_CR16","unstructured":"Manh, H., Alaghband, G.: Scene-LSTM: a model for human trajectory prediction. arXiv preprint arXiv:1808.04018 (2018)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., Claudel, C.: Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14424\u201314432 (2020)","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You\u2019ll never walk alone: modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261\u2013268. IEEE (2009)","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/978-3-642-15549-9_33","volume-title":"Computer Vision \u2013 ECCV 2010","author":"S Pellegrini","year":"2010","unstructured":"Pellegrini, S., Ess, A., Van Gool, L.: Improving data association by joint modeling of pedestrian trajectories and groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 452\u2013465. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15549-9_33"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Prabhavalkar, R., Rao, K., Sainath, T.N., Li, B., Johnson, L., Jaitly, N.: A comparison of sequence-to-sequence models for speech recognition. In: Interspeech, pp. 939\u2013943 (2017)","DOI":"10.21437\/Interspeech.2017-233"},{"key":"13_CR21","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1349\u20131358 (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Sun, J., Jiang, Q., Lu, C.: Recursive social behavior graph for trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 660\u2013669 (2020)","DOI":"10.1109\/CVPR42600.2020.00074"},{"key":"13_CR23","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. arXiv preprint arXiv:1409.3215 (2014)"},{"key":"13_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Vemula, A., Muelling, K., Oh, J.: Social attention: modeling attention in human crowds. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4601\u20134607. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460504"},{"key":"13_CR26","unstructured":"Wu, Y., et al.: Google\u2019s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)"},{"key":"13_CR27","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. arXiv preprint arXiv:2105.15203 (2021)"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Xu, Y., Piao, Z., Gao, S.: Encoding crowd interaction with deep neural network for pedestrian trajectory prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5275\u20135284 (2018)","DOI":"10.1109\/CVPR.2018.00553"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, P., Ouyang, W., Zhang, P., Xue, J., Zheng, N.: SR-LSTM: state refinement for LSTM towards pedestrian trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12085\u201312094 (2019)","DOI":"10.1109\/CVPR.2019.01236"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"}],"container-title":["Communications in Computer and Information Science","Cognitive Systems and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-9247-5_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T02:14:17Z","timestamp":1651889657000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-9247-5_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811692468","9789811692475"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-9247-5_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cognitive Systems and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Suzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccsip2021.tsingzhan.com\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","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":"41","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":"39% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}