{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T00:48:12Z","timestamp":1770425292621,"version":"3.49.0"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031918124","type":"print"},{"value":"9783031918131","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-91813-1_10","type":"book-chapter","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:41:59Z","timestamp":1748090519000},"page":"150-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LLaMAPed: Multi-modal Pedestrian Crossing Intention Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9438-5460","authenticated-orcid":false,"given":"Je-Seok","family":"Ham","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9088-7036","authenticated-orcid":false,"given":"Sunghun","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5449-3062","authenticated-orcid":false,"given":"Jia","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-1743","authenticated-orcid":false,"given":"Peng","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6616-824X","authenticated-orcid":false,"given":"Jinyoung","family":"Moon","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3906-7574","authenticated-orcid":false,"given":"Srikanth","family":"Saripalli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9323-8488","authenticated-orcid":false,"given":"Changick","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"10_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Bain, M., Nagrani, A., Varol, G., Zisserman, A.: Frozen in time: a joint video and image encoder for end-to-end retrieval. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1728\u20131738 (2021)","DOI":"10.1109\/ICCV48922.2021.00175"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, A., Fritz, M., Schiele, B.: Long-term on-board prediction of people in traffic scenes under uncertainty. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4194\u20134202 (2018)","DOI":"10.1109\/CVPR.2018.00441"},{"key":"10_CR4","unstructured":"Bouhsain, S.A., Saadatnejad, S., Alahi, A.: Pedestrian intention prediction: a multi-task perspective. arXiv preprint arXiv:2010.10270 (2020)"},{"issue":"11","key":"10_CR5","doi-asserted-by":"publisher","first-page":"21050","DOI":"10.1109\/TITS.2022.3173537","volume":"23","author":"P Cadena","year":"2022","unstructured":"Cadena, P., Qian, Y., Wang, C., Yang, M.: Pedestrian graph +: a fast pedestrian crossing prediction model based on graph convolutional networks. IEEE Trans. Intell. Transp. Syst. 23(11), 21050\u201321061 (2022). https:\/\/doi.org\/10.1109\/TITS.2022.3173537","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Changpinyo, S., Sharma, P., Ding, N., Soricut, R.: Conceptual 12M: pushing web-scale image-text pre-training to recognize long-tail visual concepts. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3558\u20133568 (2021)","DOI":"10.1109\/CVPR46437.2021.00356"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, P., Chitta, K., Jaeger, B., Geiger, A., Li, H.: End-to-end autonomous driving: challenges and frontiers. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3435937"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Chen, T.S., et\u00a0al.: Panda-70M: captioning 70M videos with multiple cross-modality teachers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13320\u201313331 (2024)","DOI":"10.1109\/CVPR52733.2024.01265"},{"key":"10_CR9","unstructured":"Cheng, Z., et\u00a0al.: VideoLLaMA 2: advancing spatial-temporal modeling and audio understanding in video-LLMs. arXiv preprint arXiv:2406.07476 (2024)"},{"key":"10_CR10","unstructured":"Dewangan, V., et al.: Talk2BEV: language-enhanced bird\u2019s-eye view maps for autonomous driving. arXiv preprint arXiv:2310.02251 (2023)"},{"key":"10_CR11","unstructured":"Dong, M.: Pedestrian cross forecasting with hybrid feature fusion. In: Asian Conference on Machine Learning, pp. 327\u2013342. PMLR (2024)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Fang, Z., L\u00f3pez, A.M.: Is the pedestrian going to cross? Answering by 2D pose estimation. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1271\u20131276. IEEE (2018)","DOI":"10.1109\/IVS.2018.8500413"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Fu, D., et al.: Drive like a human: rethinking autonomous driving with large language models. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 910\u2013919 (2024)","DOI":"10.1109\/WACVW60836.2024.00102"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Gesnouin, J., Pechberti, S., Stanciulcscu, B., Moutarde, F.: TrouSPI-net: spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction. In: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pp. 01\u201307. IEEE (2021)","DOI":"10.1109\/FG52635.2021.9666989"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Ham, J.S., Bae, K., Moon, J.: MCIP: multi-stream network for pedestrian crossing intention prediction. In: European Conference on Computer Vision, pp. 663\u2013679. Springer (2022)","DOI":"10.1007\/978-3-031-25056-9_42"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Ham, J.S., Kim, D.H., Jung, N., Moon, J.: CIPF: crossing intention prediction network based on feature fusion modules for improving pedestrian safety. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3666\u20133675 (2023)","DOI":"10.1109\/CVPRW59228.2023.00374"},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"Huang, J., Gautam, A., Saripalli, S.: Learning pedestrian actions to ensure safe autonomous driving. In: 2023 IEEE Intelligent Vehicles Symposium (IV), pp.\u00a01\u20138 (2023). https:\/\/doi.org\/10.1109\/IV55152.2023.10186530, https:\/\/ieeexplore.ieee.org\/abstract\/document\/10186530","DOI":"10.1109\/IV55152.2023.10186530"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Huang, J., Jiang, P., Gautam, A., Saripalli, S.: GPT-4v takes the wheel: evaluating promise and challenges for pedestrian behavior prediction. arXiv preprint arXiv:2311.14786 (2023)","DOI":"10.1609\/aaaiss.v3i1.31192"},{"issue":"7","key":"10_CR19","doi-asserted-by":"publisher","first-page":"7244","DOI":"10.1109\/TITS.2023.3254579","volume":"24","author":"Z Huang","year":"2023","unstructured":"Huang, Z., Liu, H., Wu, J., Lv, C.: Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving. IEEE Trans. Intell. Transp. Syst. 24(7), 7244\u20137258 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10_CR20","unstructured":"Jiang, A.Q., et\u00a0al.: Mistral 7B. arXiv preprint arXiv:2310.06825 (2023)"},{"key":"10_CR21","unstructured":"Jiang, A.Q., et\u00a0al.: Mixtral of experts. arXiv preprint arXiv:2401.04088 (2024)"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Kotseruba, I., Rasouli, A., Tsotsos, J.K.: Do they want to cross? Understanding pedestrian intention for behavior prediction. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 1688\u20131693. IEEE (2020)","DOI":"10.1109\/IV47402.2020.9304591"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Kotseruba, I., Rasouli, A., Tsotsos, J.K.: Benchmark for evaluating pedestrian action prediction. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1258\u20131268 (2021)","DOI":"10.1109\/WACV48630.2021.00130"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Liao, H., et al.: BAT: behavior-aware human-like trajectory prediction for autonomous driving. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 10332\u201310340 (2024)","DOI":"10.1609\/aaai.v38i9.28900"},{"issue":"2","key":"10_CR25","doi-asserted-by":"publisher","first-page":"3485","DOI":"10.1109\/LRA.2020.2976305","volume":"5","author":"B Liu","year":"2020","unstructured":"Liu, B., et al.: Spatiotemporal relationship reasoning for pedestrian intent prediction. IEEE Robot. Autom. Lett. 5(2), 3485\u20133492 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, C., Li, Y., Lee, Y.J.: Improved baselines with visual instruction tuning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 26296\u201326306 (2024)","DOI":"10.1109\/CVPR52733.2024.02484"},{"key":"10_CR27","unstructured":"Lorenzo, J., Parra, I., Sotelo, M.: IntFormer: predicting pedestrian intention with the aid of the transformer architecture. arXiv preprint arXiv:2105.08647 (2021)"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Peng, M., et\u00a0al.: LC-LLM: explainable lane-change intention and trajectory predictions with large language models. arXiv preprint arXiv:2403.18344 (2024)","DOI":"10.1016\/j.commtr.2025.100170"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I.: PedFormer: pedestrian behavior prediction via cross-modal attention modulation and gated multitask learning. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 9844\u20139851. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10161318"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I., Kunic, T., Tsotsos, J.K.: PIE: a large-scale dataset and models for pedestrian intention estimation and trajectory prediction. In: International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00636"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I., Tsotsos, J.K.: Agreeing to cross: how drivers and pedestrians communicate. In: 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 264\u2013269. IEEE (2017)","DOI":"10.1109\/IVS.2017.7995730"},{"key":"10_CR32","doi-asserted-by":"publisher","unstructured":"Rasouli, A., Kotseruba, I., Tsotsos, J.K.: Are they going to cross? A benchmark dataset and baseline for pedestrian crosswalk behavior. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 206\u2013213 (2017). https:\/\/doi.org\/10.1109\/ICCVW.2017.33, https:\/\/ieeexplore.ieee.org\/document\/8265243","DOI":"10.1109\/ICCVW.2017.33"},{"key":"10_CR33","unstructured":"Rasouli, A., Kotseruba, I., Tsotsos, J.K.: Pedestrian action anticipation using contextual feature fusion in stacked RNNs. arXiv preprint arXiv:2005.06582 (2020)"},{"issue":"3","key":"10_CR34","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1109\/TITS.2019.2901817","volume":"21","author":"A Rasouli","year":"2020","unstructured":"Rasouli, A., Tsotsos, J.K.: Autonomous vehicles that interact with pedestrians: a survey of theory and practice. IEEE Trans. Intell. Transp. Syst. 21(3), 900\u2013918 (2020). https:\/\/doi.org\/10.1109\/TITS.2019.2901817","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10_CR35","doi-asserted-by":"publisher","unstructured":"Razali, H., Mordan, T., Alahi, A.: Pedestrian intention prediction: a convolutional bottom-up multi-task approach. Transp. Res. Part C: Emerg. Technol. 130, 103259 (2021). https:\/\/doi.org\/10.1016\/j.trc.2021.103259","DOI":"10.1016\/j.trc.2021.103259"},{"issue":"4","key":"10_CR36","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1109\/TIV.2018.2873901","volume":"3","author":"K Saleh","year":"2018","unstructured":"Saleh, K., Hossny, M., Nahavandi, S.: Intent prediction of pedestrians via motion trajectories using stacked recurrent neural networks. IEEE Trans. Intell. Veh. 3(4), 414\u2013424 (2018). https:\/\/doi.org\/10.1109\/TIV.2018.2873901","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10_CR37","doi-asserted-by":"publisher","unstructured":"Singh, A., Suddamalla, U.: Multi-input fusion for practical pedestrian intention prediction. In: 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 2304\u20132311 (2021). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00260","DOI":"10.1109\/ICCVW54120.2021.00260"},{"key":"10_CR38","unstructured":"Sreeram, S., Wang, T.H., Maalouf, A., Rosman, G., Karaman, S., Rus, D.: Probing multimodal LLMs as world models for driving (2024)"},{"issue":"6","key":"10_CR39","doi-asserted-by":"publisher","first-page":"3692","DOI":"10.1109\/TIV.2023.3274536","volume":"8","author":"S Teng","year":"2023","unstructured":"Teng, S., et al.: Motion planning for autonomous driving: the state of the art and future perspectives. IEEE Trans. Intell. Veh. 8(6), 3692\u20133711 (2023)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Urbanek, J., Bordes, F., Astolfi, P., Williamson, M., Sharma, V., Romero-Soriano, A.: A picture is worth more than 77 text tokens: evaluating CLIP-style models on dense captions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 26700\u201326709 (2024)","DOI":"10.1109\/CVPR52733.2024.02521"},{"key":"10_CR41","unstructured":"Wang, W., Z., et\u00a0al.: DriveMLM: aligning multi-modal large language models with behavioral planning states for autonomous driving. arXiv preprint arXiv:2312.09245 (2023)"},{"key":"10_CR42","unstructured":"Wang, Y., et\u00a0al.: InternVid: a large-scale video-text dataset for multimodal understanding and generation. arXiv preprint arXiv:2307.06942 (2023)"},{"key":"10_CR43","unstructured":"Wen, L., et al.: On the road with GPT-4V(ision): explorations of utilizing visual-language model as autonomous driving agent. In: ICLR 2024 Workshop on Large Language Model (LLM) Agents (2024). https:\/\/openreview.net\/forum?id=2UBexKm8TE"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Xu, Z., et al.: DriveGPT4: interpretable end-to-end autonomous driving via large language model. arXiv preprint arXiv:2310.01412 (2023)","DOI":"10.1109\/LRA.2024.3440097"},{"issue":"6","key":"10_CR45","doi-asserted-by":"publisher","first-page":"5338","DOI":"10.1109\/TITS.2021.3053031","volume":"23","author":"B Yang","year":"2021","unstructured":"Yang, B., Zhan, W., Wang, P., Chan, C., Cai, Y., Wang, N.: Crossing or not? Context-based recognition of pedestrian crossing intention in the urban environment. IEEE Trans. Intell. Transp. Syst. 23(6), 5338\u20135349 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"10_CR46","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TIV.2022.3162719","volume":"7","author":"D Yang","year":"2022","unstructured":"Yang, D., Zhang, H., Yurtsever, E., Redmill, K.A., \u00d6zg\u00fcner, \u00dc.: Predicting pedestrian crossing intention with feature fusion and spatio-temporal attention. IEEE Trans. Intell. Veh. 7(2), 221\u2013230 (2022)","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"11","key":"10_CR47","doi-asserted-by":"publisher","first-page":"20773","DOI":"10.1109\/TITS.2022.3177367","volume":"23","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Angeloudis, P., Demiris, Y.: ST CrossingPose: a spatial-temporal graph convolutional network for skeleton-based pedestrian crossing intention prediction. IEEE Trans. Intell. Transp. Syst. 23(11), 20773\u201320782 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10_CR48","doi-asserted-by":"crossref","unstructured":"Zhao, J., et al.: Autonomous driving system: a comprehensive survey. Expert Syst. Appl. 122836 (2023)","DOI":"10.1016\/j.eswa.2023.122836"},{"key":"10_CR49","doi-asserted-by":"publisher","first-page":"93781","DOI":"10.1109\/ACCESS.2019.2927889","volume":"7","author":"J Zhao","year":"2019","unstructured":"Zhao, J., Li, Y., Xu, H., Liu, H.: Probabilistic prediction of pedestrian crossing intention using roadside lidar data. IEEE Access 7, 93781\u201393790 (2019)","journal-title":"IEEE Access"},{"key":"10_CR50","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1109\/LSP.2021.3134194","volume":"29","author":"S Zhao","year":"2021","unstructured":"Zhao, S., Li, H., Ke, Q., Liu, L., Zhang, R.: Action-ViT: pedestrian intent prediction in traffic scenes. IEEE Signal Process. Lett. 29, 324\u2013328 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"10_CR51","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tan, G., Zhong, R., Li, Y., Gou, C.: PIT: progressive interaction transformer for pedestrian crossing intention prediction. IEEE Trans. Intell. Transp. Syst. (2023)","DOI":"10.1109\/TITS.2023.3309309"},{"key":"10_CR52","unstructured":"Zhou, Y., et al.: Embodied understanding of driving scenarios. arXiv preprint arXiv:2403.04593 (2024)"},{"key":"10_CR53","unstructured":"Zhu, B., et\u00a0al.: LanguageBind: extending video-language pretraining to n-modality by language-based semantic alignment. arXiv preprint arXiv:2310.01852 (2023)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91813-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:42:10Z","timestamp":1748090530000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91813-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031918124","9783031918131"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91813-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","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"}}]}}