{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:54:46Z","timestamp":1774540486873,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"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":["J Membr Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s41965-024-00161-0","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T11:13:07Z","timestamp":1720523587000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multidimensional graph transformer networks for trajectory prediction in urban road intersections"],"prefix":"10.1007","volume":"7","author":[{"given":"Xuefeng","family":"Quan","sequence":"first","affiliation":[]},{"given":"Dening","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Tianfei","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Gexiang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"161_CR1","doi-asserted-by":"crossref","unstructured":"Goan, E., & Fookes, C. (2020). Bayesian neural networks: An introduction and survey. arXiv:2006.12024","DOI":"10.1007\/978-3-030-42553-1_3"},{"key":"161_CR2","doi-asserted-by":"publisher","unstructured":"Goli, S. A., Far, B. H., & Fapojuwo, A. O. (2018). Vehicle trajectory prediction with Gaussian process regression in connected vehicle environment$$\\star$$. In 2018 IEEE intelligent vehicles symposium (IV) (pp. 550\u2013555). https:\/\/doi.org\/10.1109\/IVS.2018.8500614","DOI":"10.1109\/IVS.2018.8500614"},{"key":"161_CR3","doi-asserted-by":"publisher","unstructured":"Ellis, D., Sommerlade, E., & Reid, I. (2009). Modelling pedestrian trajectory patterns with Gaussian processes. In 2009 IEEE 12th international conference on computer vision workshops, ICCV workshops (pp. 1229\u20131234). https:\/\/doi.org\/10.1109\/ICCVW.2009.5457470","DOI":"10.1109\/ICCVW.2009.5457470"},{"key":"161_CR4","doi-asserted-by":"crossref","unstructured":"SEEGER. (2008). MATTHIAS: Gaussian processes for machine learning. International Journal of Neural Systems, 14(02), 69\u2013106.","DOI":"10.1142\/S0129065704001899"},{"issue":"5","key":"161_CR5","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. (1995). Social force model for pedestrian dynamics. Physical Review E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 51(5), 4282\u20134286.","journal-title":"Physical Review E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics"},{"issue":"8","key":"161_CR6","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. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Computation"},{"key":"161_CR7","doi-asserted-by":"publisher","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016). Social LSTM: Human trajectory prediction in crowded spaces. In 2016 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 961\u2013971). https:\/\/doi.org\/10.1109\/CVPR.2016.110","DOI":"10.1109\/CVPR.2016.110"},{"key":"161_CR8","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/978-3-030-58536-5_45","volume-title":"Computer Vision\u2014ECCV 2020","author":"K Mangalam","year":"2020","unstructured":"Mangalam, K., Girase, H., Agarwal, S., Lee, K.-H., Adeli, E., Malik, J., & Gaidon, A. (2020). It is not the journey but the destination: Endpoint conditioned trajectory prediction. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision\u2014ECCV 2020 (pp. 759\u2013776). Cham: Springer."},{"key":"161_CR9","doi-asserted-by":"publisher","unstructured":"Kim, H., Kim, D., Kim, G., Cho, J., & Huh, K. (2020). Multi-head attention based probabilistic vehicle trajectory prediction. In 2020 IEEE intelligent vehicles symposium (IV) (pp. 1720\u20131725). https:\/\/doi.org\/10.1109\/IV47402.2020.9304741","DOI":"10.1109\/IV47402.2020.9304741"},{"key":"161_CR10","doi-asserted-by":"publisher","unstructured":"Xue, H., Huynh, D. Q., & Reynolds, M. (2018). SS-LSTM: A hierarchical LSTM model for pedestrian trajectory prediction. In 2018 IEEE winter conference on applications of computer vision (WACV) (pp. 1186\u20131194). https:\/\/doi.org\/10.1109\/WACV.2018.00135","DOI":"10.1109\/WACV.2018.00135"},{"key":"161_CR11","doi-asserted-by":"publisher","unstructured":"Messaoud, K., Yahiaoui, I., Verroust-Blondet, A., & Nashashibi, F. (2019). Non-local social pooling for vehicle trajectory prediction. In 2019 IEEE intelligent vehicles symposium (IV) (pp. 975\u2013980). https:\/\/doi.org\/10.1109\/IVS.2019.8813829","DOI":"10.1109\/IVS.2019.8813829"},{"issue":"1","key":"161_CR12","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. (2021). Attention based vehicle trajectory prediction. IEEE Transactions on Intelligent Vehicles, 6(1), 175\u2013185. https:\/\/doi.org\/10.1109\/TIV.2020.2991952","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"issue":"1","key":"161_CR13","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), 53\u201365. https:\/\/doi.org\/10.1109\/MSP.2017.2765202","journal-title":"IEEE Signal Processing Magazine"},{"key":"161_CR14","unstructured":"Kipf, T., Welling, M. (2016). Variational graph auto-encoders. arXiv:1611.07308"},{"key":"161_CR15","doi-asserted-by":"publisher","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018). Social GAN: Socially acceptable trajectories with generative adversarial networks. In 2018 IEEE\/CVF conference on computer vision and pattern recognition (pp. 2255\u20132264). https:\/\/doi.org\/10.1109\/CVPR.2018.00240","DOI":"10.1109\/CVPR.2018.00240"},{"key":"161_CR16","unstructured":"Varshneya, D., & Srinivasaraghavan, G. (2017). Human trajectory prediction using spatially aware deep attention models. arXiv:1705.09436"},{"key":"161_CR17","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., Kosaraju, V., Sadeghian, A. R., Hirose, N., & Savarese, S. (2018). Sophie: An attentive GAN for predicting paths compliant to social and physical constraints. In 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 1349\u20131358).","DOI":"10.1109\/CVPR.2019.00144"},{"key":"161_CR18","doi-asserted-by":"crossref","unstructured":"Amirian, J., Hayet, J.-B., & Pettr\u00e9, J. (2019). Social ways: Learning multi-modal distributions of pedestrian trajectories with GANs. In 2019 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW) (pp. 2964\u20132972).","DOI":"10.1109\/CVPRW.2019.00359"},{"key":"161_CR19","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: interpretable representation learning by information maximizing generative adversarial nets. In Neural information processing systems. https:\/\/api.semanticscholar.org\/CorpusID:5002792"},{"key":"161_CR20","unstructured":"Zhang, L., She, Q., & Guo, P. (2019). Stochastic trajectory prediction with social graph network. arXiv:1907.10233"},{"key":"161_CR21","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. In AAAI conference on artificial intelligence. https:\/\/api.semanticscholar.org\/CorpusID:19167105","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"161_CR22","unstructured":"Haddad, S., Wu, M., Wei, H., & Lam, S. K. (2019). Situation-aware pedestrian trajectory prediction with spatio-temporal attention model. arXiv:1902.05437"},{"key":"161_CR23","unstructured":"Kosaraju, V., Sadeghian, A., Mart\u00edn-Mart\u00edn, R., Reid, I. D., Rezatofighi, S. H., & Savarese, S. (2019). Social-bigat: Multimodal trajectory forecasting using bicycle-GAN and graph attention networks. arXiv:1907.03395"},{"key":"161_CR24","doi-asserted-by":"crossref","unstructured":"Yi, S., Li, H., & Wang, X. (2015). Understanding pedestrian behaviors from stationary crowd groups. In 2015 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3488\u20133496).","DOI":"10.1109\/CVPR.2015.7298971"},{"key":"161_CR25","doi-asserted-by":"crossref","unstructured":"Mohamed, A. A., Qian, K., Elhoseiny, M., & Claudel, C. G. (2020). Social-STGCNN: A social spatio-temporal graph convolutional neural network for human trajectory prediction. In 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 14412\u201314420).","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"161_CR26","unstructured":"Vaswani, A., Shazeer, N. M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Neural information processing systems. https:\/\/api.semanticscholar.org\/CorpusID:13756489"},{"key":"161_CR27","doi-asserted-by":"crossref","unstructured":"Giuliari, F., Hasan, I., Cristani, M., & Galasso, F. (2020). Transformer networks for trajectory forecasting. In 2020 25th international conference on pattern recognition (ICPR) (pp. 10335\u201310342).","DOI":"10.1109\/ICPR48806.2021.9412190"},{"key":"161_CR28","doi-asserted-by":"crossref","unstructured":"Yu, C., Ma, X., Ren, J., Zhao, H., & Yi, S. (2020). Spatio-temporal graph transformer networks for pedestrian trajectory prediction. arXiv:2005.08514","DOI":"10.1007\/978-3-030-58610-2_30"},{"key":"161_CR29","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/JAS.2020.1003228","volume":"7","author":"X Zhao","year":"2020","unstructured":"Zhao, X., Chen, Y., Guo, J., & Zhao, D. (2020). A spatial-temporal attention model for human trajectory prediction. IEEE\/CAA Journal of Automatica Sinica, 7, 965\u2013974.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"161_CR30","doi-asserted-by":"publisher","first-page":"7660","DOI":"10.1109\/LRA.2022.3176064","volume":"7","author":"L Zhou","year":"2022","unstructured":"Zhou, L., Yang, D., Zhai, X., Wu, S., Hu, Z., & Liu, J. (2022). GA-STT: Human trajectory prediction with group aware spatial-temporal transformer. IEEE Robotics and Automation Letters, 7, 7660\u20137667.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"161_CR31","doi-asserted-by":"crossref","unstructured":"Li, L., Pagnucco, M., & Song, Y. (2022). Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction. In 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 2221\u20132231).","DOI":"10.1109\/CVPR52688.2022.00227"},{"key":"161_CR32","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1177\/0278364920917446","volume":"39","author":"A Rudenko","year":"2019","unstructured":"Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2019). Human motion trajectory prediction: A survey. The International Journal of Robotics Research, 39, 895\u2013935.","journal-title":"The International Journal of Robotics Research"},{"key":"161_CR33","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/TVT.2021.3127008","volume":"71","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Yuan, K., Chu, H., Huang, Y., Ding, H., Yuan, J., & Chen, H. (2021). Pedestrian collision risk assessment based on state estimation and motion prediction. IEEE Transactions on Vehicular Technology, 71, 98\u2013111.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"161_CR34","doi-asserted-by":"publisher","first-page":"14458","DOI":"10.1109\/TVT.2020.3040398","volume":"69","author":"P Hang","year":"2020","unstructured":"Hang, P., Lv, C., Huang, C., Cai, J., Hu, Z., & Xing, Y. (2020). An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors. IEEE Transactions on Vehicular Technology, 69, 14458\u201314469.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"161_CR35","doi-asserted-by":"publisher","first-page":"12621","DOI":"10.1109\/TVT.2020.3027352","volume":"69","author":"C Xu","year":"2020","unstructured":"Xu, C., Wanzhong, Z., Li, L., Chen, Q., Kuang, D., & Zhou, J. (2020). A nash q-learning based motion decision algorithm with considering interaction to traffic participants. IEEE Transactions on Vehicular Technology, 69, 12621\u201312634.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"161_CR36","doi-asserted-by":"publisher","first-page":"14484","DOI":"10.1109\/TVT.2020.3041152","volume":"69","author":"GS Sankar","year":"2020","unstructured":"Sankar, G. S., & Han, K. (2020). Adaptive robust game-theoretic decision making strategy for autonomous vehicles in highway. IEEE Transactions on Vehicular Technology, 69, 14484\u201314493.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"161_CR37","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1109\/TITS.2019.2901817","volume":"21","author":"A Rasouli","year":"2018","unstructured":"Rasouli, A., & Tsotsos, J. K. (2018). Autonomous vehicles that interact with pedestrians: A survey of theory and practice. IEEE Transactions on Intelligent Transportation Systems, 21, 900\u2013918.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"161_CR38","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1109\/TIV.2020.2966117","volume":"5","author":"Y Li","year":"2020","unstructured":"Li, Y., Lu, X.-Y., Wang, J., & Li, K. (2020). Pedestrian trajectory prediction combining probabilistic reasoning and sequence learning. IEEE Transactions on Intelligent Vehicles, 5, 461\u2013474.","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"key":"161_CR39","unstructured":"Chen, W. (2022). S2tnet: Spatio-temporal transformer networks for trajectory prediction in autonomous driving. arXiv:2206.10902"},{"key":"161_CR40","doi-asserted-by":"crossref","unstructured":"Yang, Z., Han, B., Chen, W., & Gao, X. (2023). Learn to encode heterogeneous data: A heterogeneous aware network for multi-future trajectory prediction. In 2023 International joint conference on neural networks (IJCNN) (pp. 1\u20138).","DOI":"10.1109\/IJCNN54540.2023.10191508"},{"key":"161_CR41","doi-asserted-by":"publisher","first-page":"7100","DOI":"10.1109\/JIOT.2022.3228818","volume":"10","author":"J Li","year":"2023","unstructured":"Li, J., Shi, H., Guo, Y., Han, G., Yu, R., & Wang, X. (2023). Tragcan: Trajectory prediction of heterogeneous traffic agents in IoV systems. IEEE Internet of Things Journal, 10, 7100\u20137113.","journal-title":"IEEE Internet of Things Journal"},{"key":"161_CR42","doi-asserted-by":"crossref","unstructured":"Li, J., Guo, Y., Shi, H., Yu, R., Wang, X., & Hou, X. (2022). Heterogeneous traffic trajectory prediction via spatial attention network for internet of vehicles. In 2022 8th international conference on big data computing and communications (BigCom) (pp. 285\u2013293).","DOI":"10.1109\/BigCom57025.2022.00043"},{"key":"161_CR43","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.3390\/math11081923","volume":"11","author":"A-I Patachi","year":"2023","unstructured":"Patachi, A.-I., & Leon, F. (2023). Multiagent multimodal trajectory prediction in urban traffic scenarios using a neural network-based solution. Mathematics, 11, 1923.","journal-title":"Mathematics"},{"key":"161_CR44","doi-asserted-by":"crossref","unstructured":"Liu, S., Chen, X., Wu, Z., Deng, L., Su, H., & Zheng, K. (2022). Hega: Heterogeneous graph aggregation network for trajectory prediction in high-density traffic. In Proceedings of the 31st ACM international conference on information & knowledge management.","DOI":"10.1145\/3511808.3557345"},{"key":"161_CR45","doi-asserted-by":"crossref","unstructured":"Chandra, R., Bhattacharya, U., Bera, A., & Manocha, D. (2018). Traphic: Trajectory prediction in dense and heterogeneous traffic using weighted interactions. In 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 8475\u20138484).","DOI":"10.1109\/CVPR.2019.00868"},{"key":"161_CR46","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhu, X., Zhang, S., Yang, R., Wang, W., & Manocha, D. (2018). Trafficpredict: Trajectory prediction for heterogeneous traffic-agents. arXiv:1811.02146","DOI":"10.1609\/aaai.v33i01.33016120"},{"key":"161_CR47","doi-asserted-by":"publisher","first-page":"6217","DOI":"10.1109\/TITS.2023.3248090","volume":"24","author":"D Xu","year":"2023","unstructured":"Xu, D., Shang, X., Peng, H., & Li, H. (2023). Mvhgn: Multi-view adaptive hierarchical spatial graph convolution network based trajectory prediction for heterogeneous traffic-agents. IEEE Transactions on Intelligent Transportation Systems, 24, 6217\u20136226.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"161_CR48","doi-asserted-by":"publisher","unstructured":"Xuan, W., Ren, R., & Wang, C. (2021). Multi-agent interactive prediction under challenging driving scenarios. In 2021 7th international conference on control, automation and robotics (ICCAR) (pp. 6\u201313). https:\/\/doi.org\/10.1109\/ICCAR52225.2021.9463434","DOI":"10.1109\/ICCAR52225.2021.9463434"},{"issue":"3","key":"161_CR49","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1109\/TMECH.2021.3073736","volume":"26","author":"Z Li","year":"2021","unstructured":"Li, Z., Gong, J., Lu, C., & Yi, Y. (2021). Interactive behavior prediction for heterogeneous traffic participants in the urban road: A graph-neural-network-based multitask learning framework. IEEE\/ASME Transactions on Mechatronics, 26(3), 1339\u20131349. https:\/\/doi.org\/10.1109\/TMECH.2021.3073736","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"161_CR50","doi-asserted-by":"crossref","unstructured":"Grimm, D., Zipfl, M., Hertlein, F., Naumann, A., L\u00fcttin, J., Thoma, S., Schmid, S., Halilaj, L., Rettinger, A., & Z\u00f6llner, J. M. (2023). Heterogeneous graph-based trajectory prediction using local map context and social interactions. In 2023 IEEE 26th international conference on intelligent transportation systems (ITSC) (pp. 2901\u20132907).","DOI":"10.1109\/ITSC57777.2023.10422462"}],"container-title":["Journal of Membrane Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-024-00161-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41965-024-00161-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-024-00161-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T08:08:13Z","timestamp":1740989293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41965-024-00161-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,9]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["161"],"URL":"https:\/\/doi.org\/10.1007\/s41965-024-00161-0","relation":{},"ISSN":["2523-8906","2523-8914"],"issn-type":[{"value":"2523-8906","type":"print"},{"value":"2523-8914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,9]]},"assertion":[{"value":"5 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}