{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T20:33:24Z","timestamp":1754598804918,"version":"3.40.3"},"publisher-location":"Cham","reference-count":68,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031726231"},{"type":"electronic","value":"9783031726248"}],"license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"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\/978-3-031-72624-8_16","type":"book-chapter","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T09:52:13Z","timestamp":1729849933000},"page":"270-287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Risk-Aware Self-consistent Imitation Learning for\u00a0Trajectory Planning in\u00a0Autonomous Driving"],"prefix":"10.1007","author":[{"given":"Yixuan","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yali","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengjin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12532-018-0139-4","volume":"11","author":"JA Andersson","year":"2019","unstructured":"Andersson, J.A., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADI: a software framework for nonlinear optimization and optimal control. Math. Program. Comput. 11, 1\u201336 (2019)","journal-title":"Math. Program. Comput."},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Bansal, M., Krizhevsky, A., Ogale, A.: ChauffeurNet: learning to drive by imitating the best and synthesizing the worst. arXiv preprint arXiv:1812.03079 (2018)","DOI":"10.15607\/RSS.2019.XV.031"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: CVPR, pp. 11621\u201311631 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"16_CR4","unstructured":"Caesar, H., et al.: nuPlan: a closed-loop ml-based planning benchmark for autonomous vehicles. arXiv preprint arXiv:2106.11810 (2021)"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Casas, S., Sadat, A., Urtasun, R.: MP3: a unified model to map, perceive, predict and plan. In: CVPR, pp. 14403\u201314412 (2021)","DOI":"10.1109\/CVPR46437.2021.01417"},{"key":"16_CR6","unstructured":"Chai, Y., Sapp, B., Bansal, M., Anguelov, D.: Multipath: multiple probabilistifc anchor trajectory hypotheses for behavior prediction. arXiv preprint arXiv:1910.05449 (2019)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Chang, M.F., et\u00a0al.: Argoverse: 3D tracking and forecasting with rich maps. In: CVPR, pp. 8748\u20138757 (2019)","DOI":"10.1109\/CVPR.2019.00895"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Chen, D., Kr\u00e4henb\u00fchl, P.: Learning from all vehicles. In: CVPR, pp. 17222\u201317231 (2022)","DOI":"10.1109\/CVPR52688.2022.01671"},{"key":"16_CR9","unstructured":"Chen, D., Zhou, B., Koltun, V., Kr\u00e4henb\u00fchl, P.: Learning by cheating. In: CoRL, pp. 66\u201375. PMLR (2020)"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, J., Chen, Y., Mei, X., Yang, B., Li, B., Liu, M.: Rethinking imitation-based planner for autonomous driving. arXiv preprint arXiv:2309.10443 (2023)","DOI":"10.1109\/ICRA57147.2024.10611364"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Codevilla, F., Lopez, A.M., Koltun, V., Dosovitskiy, A.: On offline evaluation of vision-based driving models. In: ECCV, pp. 236\u2013251 (2018)","DOI":"10.1007\/978-3-030-01267-0_15"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Codevilla, F., Santana, E., L\u00f3pez, A.M., Gaidon, A.: Exploring the limitations of behavior cloning for autonomous driving. In: ICCV, pp. 9329\u20139338 (2019)","DOI":"10.1109\/ICCV.2019.00942"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Cui, H., et al.: Multimodal trajectory predictions for autonomous driving using deep convolutional networks. In: ICRA, pp. 2090\u20132096. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793868"},{"key":"16_CR14","unstructured":"Dauner, D., Hallgarten, M., Geiger, A., Chitta, K.: Parting with misconceptions about learning-based vehicle motion planning. arXiv preprint arXiv:2306.07962 (2023)"},{"key":"16_CR15","unstructured":"Deo, N., Wolff, E., Beijbom, O.: Multimodal trajectory prediction conditioned on lane-graph traversals. In: CoRL, pp. 203\u2013212. PMLR (2022)"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Ettinger, S., et\u00a0al.: Large scale interactive motion forecasting for autonomous driving: the Waymo open motion dataset. In: ICCV, pp. 9710\u20139719 (2021)","DOI":"10.1109\/ICCV48922.2021.00957"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Fan, Y., Liu, X., Li, Y., Wang, S.: Look before you drive: boosting trajectory forecasting via imagining future. In: IROS, pp. 5551\u20135558. IEEE (2023)","DOI":"10.1109\/IROS55552.2023.10341509"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Gao, J., et al.: VectorNet: encoding HD maps and agent dynamics from vectorized representation. In: CVPR, pp. 11525\u201311533 (2020)","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., Moutarde, F.: GOHOME: graph-oriented heatmap output for future motion estimation. In: ICRA, pp. 9107\u20139114. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9812253"},{"key":"16_CR20","unstructured":"Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., Moutarde, F.: THOMAS: trajectory heatmap output with learned multi-agent sampling. In: ICLR (2022)"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Guo, K., Jing, W., Chen, J., Pan, J.: CCIL: context-conditioned imitation learning for urban driving. arXiv preprint arXiv:2305.02649 (2023)","DOI":"10.15607\/RSS.2023.XIX.101"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Hallgarten, M., Stoll, M., Zell, A.: From prediction to planning with goal conditioned lane graph traversals. arXiv preprint arXiv:2302.07753 (2023)","DOI":"10.1109\/ITSC57777.2023.10421854"},{"key":"16_CR23","unstructured":"Hayward, J.C.: Near miss determination through use of a scale of danger (1972)"},{"issue":"8","key":"16_CR24","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(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Hong, J., Sapp, B., Philbin, J.: Rules of the road: predicting driving behavior with a convolutional model of semantic interactions. In: CVPR, pp. 8454\u20138462 (2019)","DOI":"10.1109\/CVPR.2019.00865"},{"key":"16_CR26","unstructured":"Van\u00a0der Horst, A., Hogema, J.: Time-to-collision and collision avoidance systems. Verkeersgedrag in Onderzoek (1994)"},{"key":"16_CR27","unstructured":"Hu, Y., et al.: Imitation with spatial-temporal heatmap: 2nd place solution for nuPlan challenge. In: CVPRW (2023)"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Hu, Y., et\u00a0al.: Planning-oriented autonomous driving. In: CVPR, pp. 17853\u201317862 (2023)","DOI":"10.1109\/CVPR52729.2023.01712"},{"key":"16_CR29","unstructured":"Huang, Z., Liu, H., Mo, X., Lyu, C.: Gameformer planner: a learning-enabled interactive prediction and planning framework for autonomous vehicles. In: CVPRW (2023)"},{"key":"16_CR30","unstructured":"Jia, X., Sun, L., Zhao, H., Tomizuka, M., Zhan, W.: Multi-agent trajectory prediction by combining egocentric and allocentric views. In: CoRL, pp. 1434\u20131443. PMLR (2022)"},{"key":"16_CR31","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 TPAMI (2023)","DOI":"10.1109\/TPAMI.2023.3298301"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Jiang, B., et al.: VAD: vectorized scene representation for efficient autonomous driving. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00766"},{"key":"16_CR33","unstructured":"Khandelwal, S., Qi, W., Singh, J., Hartnett, A., Ramanan, D.: What-if motion prediction for autonomous driving. arXiv preprint arXiv:2008.10587 (2020)"},{"key":"16_CR34","unstructured":"Konev, S., Brodt, K., Sanakoyeu, A.: MotionCNN: a strong baseline for motion prediction in autonomous driving. arXiv preprint arXiv:2206.02163 (2022)"},{"issue":"4","key":"16_CR35","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1068\/p050437","volume":"5","author":"DN Lee","year":"1976","unstructured":"Lee, D.N.: A theory of visual control of braking based on information about time-to-collision. Perception 5(4), 437\u2013459 (1976)","journal-title":"Perception"},{"key":"16_CR36","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: CVPR, pp. 336\u2013345 (2017)","DOI":"10.1109\/CVPR.2017.233"},{"key":"16_CR37","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":"16_CR38","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, J., Fang, L., Jiang, Q., Zhou, B.: Multimodal motion prediction with stacked transformers. In: CVPR, pp. 7577\u20137586 (2021)","DOI":"10.1109\/CVPR46437.2021.00749"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Mercat, J., Gilles, T., El\u00a0Zoghby, N., Sandou, G., Beauvois, D., Gil, G.P.: Multi-head attention for multi-modal joint vehicle motion forecasting. In: ICRA, pp. 9638\u20139644. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9197340"},{"issue":"7","key":"16_CR41","first-page":"9554","volume":"23","author":"X Mo","year":"2022","unstructured":"Mo, X., Huang, Z., Xing, Y., Lv, C.: Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network. IEEE TITS 23(7), 9554\u20139567 (2022)","journal-title":"IEEE TITS"},{"key":"16_CR42","unstructured":"Ngiam, J., et\u00a0al.: Scene transformer: a unified architecture for predicting future trajectories of multiple agents. In: ICLR (2022)"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Ohn-Bar, E., Prakash, A., Behl, A., Chitta, K., Geiger, A.: Learning situational driving. In: CVPR, pp. 11296\u201311305 (2020)","DOI":"10.1109\/CVPR42600.2020.01131"},{"key":"16_CR44","unstructured":"Phan-Minh, T., et\u00a0al.: Driving in real life with inverse reinforcement learning. arXiv preprint arXiv:2206.03004 (2022)"},{"key":"16_CR45","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: Deep learning on point sets for 3D classification and segmentation. In: CVPR, pp. 652\u2013660 (2017)"},{"key":"16_CR46","unstructured":"Renz, K., Chitta, K., Mercea, O.B., Koepke, A., Akata, Z., Geiger, A.: PlanT: explainable planning transformers via object-level representations. arXiv preprint arXiv:2210.14222 (2022)"},{"key":"16_CR47","unstructured":"Rhinehart, N., McAllister, R., Levine, S.: Deep imitative models for flexible inference, planning, and control. arXiv preprint arXiv:1810.06544 (2018)"},{"key":"16_CR48","unstructured":"Ross, S., Gordon, G., Bagnell, D.: A reduction of imitation learning and structured prediction to no-regret online learning. In: AISTATS, pp. 627\u2013635. JMLR Workshop and Conference Proceedings (2011)"},{"key":"16_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/978-3-030-58523-5_40","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Salzmann","year":"2020","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 683\u2013700. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_40"},{"key":"16_CR50","unstructured":"Scheel, O., Bergamini, L., Wolczyk, M., Osi\u0144ski, B., Ondruska, P.: Urban driver: learning to drive from real-world demonstrations using policy gradients. In: CoRL, pp. 718\u2013728. PMLR (2022)"},{"key":"16_CR51","unstructured":"Shao, H., Wang, L., Chen, R., Li, H., Liu, Y.: Safety-enhanced autonomous driving using interpretable sensor fusion transformer. In: CoRL, pp. 726\u2013737. PMLR (2023)"},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: CVPR, pp. 761\u2013769 (2016)","DOI":"10.1109\/CVPR.2016.89"},{"issue":"2","key":"16_CR53","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1103\/PhysRevE.62.1805","volume":"62","author":"M Treiber","year":"2000","unstructured":"Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62(2), 1805 (2000)","journal-title":"Phys. Rev. E"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Varadarajan, B., et\u00a0al.: MultiPath++: efficient information fusion and trajectory aggregation for behavior prediction. In: ICRA, pp. 7814\u20137821. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9812107"},{"key":"16_CR55","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS, vol. 30 (2017)"},{"issue":"3","key":"16_CR56","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/S0001-4575(02)00022-2","volume":"35","author":"K Vogel","year":"2003","unstructured":"Vogel, K.: A comparison of headway and time to collision as safety indicators. Accid. Anal. Prevent. 35(3), 427\u2013433 (2003)","journal-title":"Accid. Anal. Prevent."},{"key":"16_CR57","unstructured":"Wen, C., Lin, J., Darrell, T., Jayaraman, D., Gao, Y.: Fighting copycat agents in behavioral cloning from observation histories. In: NeurIPS, vol. 33, pp. 2564\u20132575 (2020)"},{"key":"16_CR58","unstructured":"Wilson, B., et\u00a0al.: Argoverse 2: next generation datasets for self-driving perception and forecasting. arXiv preprint arXiv:2301.00493 (2023)"},{"key":"16_CR59","unstructured":"Wu, H., Phong, T., Yu, C., Cai, P., Zheng, S., Hsu, D.: What truly matters in trajectory prediction for autonomous driving? arXiv preprint arXiv:2306.15136 (2023)"},{"key":"16_CR60","unstructured":"Wu, P., Jia, X., Chen, L., Yan, J., Li, H., Qiao, Y.: Trajectory-guided control prediction for end-to-end autonomous driving: a simple yet strong baseline. In: NeurIPS, vol. 35, pp. 6119\u20136132 (2022)"},{"key":"16_CR61","unstructured":"Xi, W., Shi, L., Cao, G.: An imitation learning method with data augmentation and post processing for planning in autonomous driving. In: CVPRW (2023)"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Xu, H., Gao, Y., Yu, F., Darrell, T.: End-to-end learning of driving models from large-scale video datasets. In: CVPR, pp. 2174\u20132182 (2017)","DOI":"10.1109\/CVPR.2017.376"},{"key":"16_CR63","doi-asserted-by":"crossref","unstructured":"Ye, M., Cao, T., Chen, Q.: TPCN: temporal point cloud networks for motion forecasting. In: CVPR, pp. 11318\u201311327 (2021)","DOI":"10.1109\/CVPR46437.2021.01116"},{"key":"16_CR64","doi-asserted-by":"crossref","unstructured":"Zeng, W., Liang, M., Liao, R., Urtasun, R.: LanerCNN: distributed representations for graph-centric motion forecasting. In: IROS, pp. 532\u2013539. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9636035"},{"key":"16_CR65","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/978-3-030-58589-1_10","volume-title":"Computer Vision \u2013 ECCV 2020","author":"W Zeng","year":"2020","unstructured":"Zeng, W., Wang, S., Liao, R., Chen, Y., Yang, B., Urtasun, R.: DSDNet: deep structured self-driving network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 156\u2013172. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58589-1_10"},{"key":"16_CR66","unstructured":"Zhai, J.T., et al.: Rethinking the open-loop evaluation of end-to-end autonomous driving in nuScenes. arXiv preprint arXiv:2305.10430 (2023)"},{"key":"16_CR67","doi-asserted-by":"crossref","unstructured":"Zhou, J., et al.: Exploring imitation learning for autonomous driving with feedback synthesizer and differentiable rasterization. In: IROS, pp. 1450\u20131457. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9636795"},{"key":"16_CR68","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, pp. 8823\u20138833 (2022)","DOI":"10.1109\/CVPR52688.2022.00862"}],"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\/978-3-031-72624-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T07:45:52Z","timestamp":1732952752000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72624-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"ISBN":["9783031726231","9783031726248"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72624-8_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"26 October 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"}}]}}