{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:02:02Z","timestamp":1780430522243,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819666874","type":"print"},{"value":"9789819666881","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"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":[[2026]]},"DOI":"10.1007\/978-981-96-6688-1_10","type":"book-chapter","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T07:28:54Z","timestamp":1753082934000},"page":"137-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Conditional Diffusion Model for\u00a0Accurate Pedestrian Trajectory Prediction"],"prefix":"10.1007","author":[{"given":"Rui","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghui","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mukesh","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Huo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","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. IEEE (2023)","DOI":"10.1109\/IV55152.2023.10186530"},{"issue":"5","key":"10_CR2","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.3390\/s23052773","volume":"23","author":"E Moreno","year":"2023","unstructured":"Moreno, E., Denny, P., Ward, E., Horgan, J., Eising, C., Jones, E., Glavin, M., Parsi, A., Mullins, D., Deegan, B.: Pedestrian crossing intention forecasting at unsignalized intersections using naturalistic trajectories. Sensors 23(5), 2773 (2023)","journal-title":"Sensors"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wang, L., Zhou, S., Duan, J., Hua, G., Tang, W.: Multi-stream representation learning for pedestrian trajectory prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2875\u20132882 (2023)","DOI":"10.1609\/aaai.v37i3.25389"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Kesa, O., Styles, O., Sanchez, V.: Multiple object tracking and forecasting: Jointly predicting current and future object locations. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 560\u2013569 (2022)","DOI":"10.1109\/WACVW54805.2022.00062"},{"key":"10_CR5","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":"10_CR6","doi-asserted-by":"crossref","unstructured":"Gu, T., Chen, G., Li, J., Lin, C., Rao, Y., Zhou, J., Lu, J.: Stochastic trajectory prediction via motion indeterminacy diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 17113\u201317122 (2022)","DOI":"10.1109\/CVPR52688.2022.01660"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Xu, P., Hayet, J.B., Karamouzas, I.: Socialvae: Human trajectory prediction using timewise latents. In: European Conference on Computer Vision, pp. 511\u2013528. Springer (2022)","DOI":"10.1007\/978-3-031-19772-7_30"},{"key":"10_CR8","unstructured":"Kosaraju, V., Sadeghian, A., Mart\u00edn-Mart\u00edn, R., Reid, I., Rezatofighi, H., Savarese, S.: Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. Advances in Neural Information Processing Systems 32 (2019)"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Amirian, J., Hayet, J.B., Pettr\u00e9, J.: Social ways: Learning multi-modal distributions of pedestrian trajectories with gans. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. pp.\u00a00\u20130 (2019)","DOI":"10.1109\/CVPRW.2019.00359"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Dendorfer, P., Elflein, S., Leal-Taix\u00e9, L.: Mg-gan: A multi-generator model preventing out-of-distribution samples in pedestrian trajectory prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13158\u201313167 (2021)","DOI":"10.1109\/ICCV48922.2021.01291"},{"key":"10_CR11","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 336\u2013345 (2017)","DOI":"10.1109\/CVPR.2017.233"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Weng, X., Ou, Y., Kitani, K.M.: Agentformer: agent-aware transformers for socio-temporal multi-agent forecasting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9813\u20139823 (2021)","DOI":"10.1109\/ICCV48922.2021.00967"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Chen, G., Li, J., Zhou, N., Ren, L., Lu, J.: Personalized trajectory prediction via distribution discrimination. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15580\u201315589 (2021)","DOI":"10.1109\/ICCV48922.2021.01529"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Halawa, M., Hellwich, O., Bideau, P.: Action-based contrastive learning for trajectory prediction. In: European Conference on Computer Vision, pp. 143\u2013159. Springer (2022)","DOI":"10.1007\/978-3-031-19842-7_9"},{"key":"10_CR15","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10_CR17","unstructured":"Ho, J., et\u00a0al.: Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303 (2022)"},{"issue":"10","key":"10_CR18","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.3390\/e25101469","volume":"25","author":"R Yang","year":"2023","unstructured":"Yang, R., Srivastava, P., Mandt, S.: Diffusion probabilistic modeling for video generation. Entropy 25(10), 1469 (2023)","journal-title":"Entropy"},{"key":"10_CR19","unstructured":"Chen, N., Zhang, Y., Zen, H., Weiss, R.J., Norouzi, M., Chan, W.: Wavegrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713 (2020)"},{"key":"10_CR20","unstructured":"Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: Diffwave: a versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020)"},{"key":"10_CR21","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Xue, H., Huynh, D.Q., Reynolds, M.: Ss-lstm: a hierarchical lstm model for pedestrian trajectory prediction. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1186\u20131194. IEEE (2018)","DOI":"10.1109\/WACV.2018.00135"},{"key":"10_CR23","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":"10_CR24","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":"10_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-030-58610-2_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Yu","year":"2020","unstructured":"Yu, C., Ma, X., Ren, J., Zhao, H., Yi, S.: Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 507\u2013523. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_30"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Chen, G., Li, J., Lu, J., Zhou, J.: Human trajectory prediction via counterfactual analysis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9824\u20139833 (2021)","DOI":"10.1109\/ICCV48922.2021.00968"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer graphics forum. pp. 655\u2013664. Wiley Online Library (2007)","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Li, J., Ma, H., Tomizuka, M.: Conditional generative neural system for probabilistic trajectory prediction. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6150\u20136156. IEEE (2019)","DOI":"10.1109\/IROS40897.2019.8967822"},{"key":"10_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/978-3-030-58536-5_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"K Mangalam","year":"2020","unstructured":"Mangalam, K., Girase, H., Agarwal, S., Lee, K.-H., Adeli, E., Malik, J., Gaidon, A.: It is not the journey but the destination: endpoint conditioned trajectory prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 759\u2013776. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_45"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Duan, J., Wang, L., Long, C., Zhou, S., Zheng, F., Shi, L., Hua, G.: Complementary attention gated network for pedestrian trajectory prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 542\u2013550 (2022)","DOI":"10.1609\/aaai.v36i1.19933"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6688-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T19:11:44Z","timestamp":1780427504000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6688-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,22]]},"ISBN":["9789819666874","9789819666881"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6688-1_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,22]]},"assertion":[{"value":"22 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","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":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}