{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T04:01:00Z","timestamp":1785902460221,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,3]]},"DOI":"10.1145\/3769102.3774636","type":"proceedings-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:00:41Z","timestamp":1764777641000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Communication-aware Diffusion Models for Multi-Agent Trajectory Forecasting in Connected and Autonomous Vehicles"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5949-8517","authenticated-orcid":false,"given":"Jingyi","family":"He","sequence":"first","affiliation":[{"name":"Civil and Environmental Engineering, University of Washington, Seattle, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8836-1596","authenticated-orcid":false,"given":"Kehua","family":"Chen","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, University of Washington, Seattle, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4699-6409","authenticated-orcid":false,"given":"Bingzhang","family":"Wang","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, University of Washington, Seattle, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4180-5628","authenticated-orcid":false,"given":"Yinhai","family":"Wang","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, University of Washington, Seattle, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23041963"},{"key":"e_1_3_2_1_2_1","volume-title":"Conference on robot learning. PMLR, 947-956","author":"Casas Sergio","year":"2018","unstructured":"Sergio Casas, Wenjie Luo, and Raquel Urtasun. 2018. Intentnet: Learning to predict intention from raw sensor data. In Conference on robot learning. PMLR, 947-956."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3332675"},{"key":"e_1_3_2_1_4_1","volume-title":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 746-751","author":"Chen Kehua","year":"2023","unstructured":"Kehua Chen, Xianda Chen, Zihan Yu, Meixin Zhu, and Hai Yang. 2023. Equidiff: A conditional equivariant diffusion model for trajectory prediction. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 746-751."},{"key":"e_1_3_2_1_5_1","volume-title":"Human-Like Interactive Lane-Change Modeling Based on Reward-Guided Diffusive Predictor and Planner","author":"Chen Kehua","year":"2024","unstructured":"Kehua Chen, Yuhao Luo, Meixin Zhu, and Hai Yang. 2024. Human-Like Interactive Lane-Change Modeling Based on Reward-Guided Diffusive Predictor and Planner. IEEE Transactions on Intelligent Transportation Systems (2024)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22155535"},{"key":"e_1_3_2_1_7_1","volume-title":"Denoising diffusion probabilistic models. Advances in neural information processing systems 33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840-6851."},{"key":"e_1_3_2_1_8_1","volume-title":"Hongyu Sun, Quan Zhou, and Hongming Xu.","author":"Hua Min","year":"2025","unstructured":"Min Hua, Xinda Qi, Dong Chen, Kun Jiang, Zemin Eitan Liu, Hongyu Sun, Quan Zhou, and Hongming Xu. 2025. Multi-agent reinforcement learning for connected and automated vehicles control: Recent advancements and future prospects. IEEE Transactions on Automation Science and Engineering (2025)."},{"key":"e_1_3_2_1_9_1","volume-title":"Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991","author":"Janner Michael","year":"2022","unstructured":"Michael Janner, Yilun Du, Joshua B Tenenbaum, and Sergey Levine. 2022. Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991 (2022)."},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 9644-9653","author":"Jiang Chiyu","year":"2023","unstructured":"Chiyu Jiang, Andre Cornman, Cheolho Park, Benjamin Sapp, Yin Zhou, Dragomir Anguelov, et al. 2023. Motiondiffuser: Controllable multi-agent motion prediction using diffusion. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 9644-9653."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1109\/TCCN.2020.3003036","article-title":"Deep reinforcement learning for collaborative edge computing in vehicular networks","volume":"6","author":"Li Mushu","year":"2020","unstructured":"Mushu Li, Jie Gao, Lian Zhao, and Xuemin Shen. 2020. Deep reinforcement learning for collaborative edge computing in vehicular networks. IEEE Transactions on Cognitive Communications and Networking 6, 4 (2020), 1122-1135.","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","first-page":"105676","DOI":"10.1016\/j.aap.2020.105676","article-title":"Analysis of the transition condition of rear-end collisions using time-to-collision index and vehicle trajectory data","volume":"144","author":"Li Ye","year":"2020","unstructured":"Ye Li, Dan Wu, Jaeyoung Lee, Min Yang, and Yuntao Shi. 2020. Analysis of the transition condition of rear-end collisions using time-to-collision index and vehicle trajectory data. Accident Analysis & Prevention 144 (2020), 105676.","journal-title":"Accident Analysis & Prevention"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2915983"},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 7577-7586","author":"Liu Yicheng","year":"2021","unstructured":"Yicheng Liu, Jinghuai Zhang, Liangji Fang, Qinhong Jiang, and Bolei Zhou. 2021. Multimodal motion prediction with stacked transformers. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 7577-7586."},{"key":"e_1_3_2_1_15_1","volume-title":"Conference on Robot Learning. PMLR, 1457-1467","author":"Luo Wenjie","year":"2023","unstructured":"Wenjie Luo, Cheol Park, Andre Cornman, Benjamin Sapp, and Dragomir Anguelov. 2023. Jfp: Joint future prediction with interactive multi-agent modeling for autonomous driving. In Conference on Robot Learning. PMLR, 1457-1467."},{"key":"e_1_3_2_1_16_1","volume-title":"Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, et al.","author":"Ngiam Jiquan","year":"2021","unstructured":"Jiquan Ngiam, Benjamin Caine, Vijay Vasudevan, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, et al. 2021. Scene transformer: A unified architecture for predicting multiple agent trajectories. arXiv preprint arXiv:2106.08417 (2021)."},{"key":"e_1_3_2_1_17_1","volume-title":"European Conference on Computer Vision. Springer, 683-700","author":"Salzmann Tim","year":"2020","unstructured":"Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone. 2020. Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. In European Conference on Computer Vision. Springer, 683-700."},{"key":"e_1_3_2_1_18_1","volume-title":"Dusit Niyato, Xueqiang Yan, and Xu Chen.","author":"Wang Xiaofei","year":"2020","unstructured":"Xiaofei Wang, Yiwen Han, Victor CM Leung, Dusit Niyato, Xueqiang Yan, and Xu Chen. 2020. Convergence of edge computing and deep learning: A comprehensive survey. IEEE communications surveys & tutorials 22, 2 (2020), 869-904."},{"key":"e_1_3_2_1_19_1","volume-title":"Diffusion-based environment-aware trajectory prediction. arXiv preprint arXiv:2403.11643","author":"Westny Theodor","year":"2024","unstructured":"Theodor Westny, Bj\u00f6rn Olofsson, and Erik Frisk. 2024. Diffusion-based environment-aware trajectory prediction. arXiv preprint arXiv:2403.11643 (2024)."},{"key":"e_1_3_2_1_20_1","volume-title":"Diffusion-es: Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following. arXiv preprint arXiv:2402.06559","author":"Yang Brian","year":"2024","unstructured":"Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke, Ayush Jain, Jeff Schneider, and Katerina Fragkiadaki. 2024. Diffusion-es: Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following. arXiv preprint arXiv:2402.06559 (2024)."},{"key":"e_1_3_2_1_21_1","volume-title":"2021 IEEE\/ACM symposium on edge computing (SEC). IEEE, 371-375","author":"Yu Ruozhou","year":"2021","unstructured":"Ruozhou Yu, Dejun Yang, and Hao Zhang. 2021. Edge-assisted collaborative perception in autonomous driving: A reflection on communication design. In 2021 IEEE\/ACM symposium on edge computing (SEC). IEEE, 371-375."},{"key":"e_1_3_2_1_22_1","volume-title":"Arnaud de La Fortelle, et al","author":"Zhan Wei","year":"2019","unstructured":"Wei Zhan, Liting Sun, Di Wang, Haojie Shi, Aubrey Clausse, Maximilian Naumann, Julius Kummerle, Hendrik Konigshof, Christoph Stiller, Arnaud de La Fortelle, et al. 2019. Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps. arXiv preprint arXiv:1910.03088 (2019)."},{"key":"e_1_3_2_1_23_1","volume-title":"Conference on robot learning. PMLR, 895-904","author":"Zhao Hang","year":"2021","unstructured":"Hang Zhao, Jiyang Gao, Tian Lan, Chen Sun, Ben Sapp, Balakrishnan Varadarajan, Yue Shen, Yi Shen, Yuning Chai, Cordelia Schmid, et al. 2021. Tnt: Target-driven trajectory prediction. In Conference on robot learning. PMLR, 895-904."}],"event":{"name":"SEC '25: Tenth ACM\/IEEE Symposium on Edge Computing","location":"the Hilton Arlington National Landing Arlington VA USA","acronym":"SEC '25","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","IEEE Computer Society"]},"container-title":["Proceedings of the Tenth ACM\/IEEE Symposium on Edge Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769102.3774636","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:04:24Z","timestamp":1764777864000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769102.3774636"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"references-count":23,"alternative-id":["10.1145\/3769102.3774636","10.1145\/3769102"],"URL":"https:\/\/doi.org\/10.1145\/3769102.3774636","relation":{},"subject":[],"published":{"date-parts":[[2025,12,3]]},"assertion":[{"value":"2025-12-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}