{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T03:09:46Z","timestamp":1769310586162,"version":"3.49.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["GetMobile: Mobile Comp. and Comm."],"published-print":{"date-parts":[[2023,5,17]]},"abstract":"<jats:p>Infrastructure-assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle's perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-time (up to 100ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects' lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication\/ compute delays.<\/jats:p>","DOI":"10.1145\/3599184.3599193","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T23:52:21Z","timestamp":1684799541000},"page":"28-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["VIPS: Real-Time Perception Fusion for Infrastructure-Assisted Autonomous Driving"],"prefix":"10.1145","volume":"27","author":[{"given":"Shuyao","family":"Shi","sequence":"first","affiliation":[{"name":"Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahe","family":"Cui","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhehao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Yan","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Xing","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niu","family":"Jianwei","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ouyang","family":"Zhenchao","sequence":"additional","affiliation":[{"name":"Beihang Hangzhou Innovation Institute Yuhang, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Conference on Robot Learning. PMLR, 1-16","author":"Dosovitskiy Alexey","year":"2017","unstructured":"Alexey Dosovitskiy , German Ros , Felipe Codevilla , Antonio Lopez , and Vladlen Koltun . 2017 . CARLA: An open urban driving simulator . Conference on Robot Learning. PMLR, 1-16 . Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017. CARLA: An open urban driving simulator. Conference on Robot Learning. PMLR, 1-16."},{"key":"e_1_2_1_2_1","volume-title":"IEEE International Conference on Robotics and Automation (ICRA). IEEE, 11054--11059","author":"Koide Kenji","year":"2021","unstructured":"Kenji Koide , Masashi Yokozuka , Shuji Oishi , and Atsuhiko Banno . 2021 . Voxelized gicp for fast and accurate 3d point cloud registration. 2021 . IEEE International Conference on Robotics and Automation (ICRA). IEEE, 11054--11059 . Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno. 2021.Voxelized gicp for fast and accurate 3d point cloud registration. 2021. IEEE International Conference on Robotics and Automation (ICRA). IEEE, 11054--11059."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"e_1_2_1_4_1","article-title":"Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors","author":"Arnold Eduardo","year":"2020","unstructured":"Eduardo Arnold , Mehrdad Dianati , Robert de Temple , and Saber Fallah . 2020 . Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors . IEEE Transactions on Intelligent Transportation Systems. Eduardo Arnold, Mehrdad Dianati, Robert de Temple, and Saber Fallah. 2020. Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors. IEEE Transactions on Intelligent Transportation Systems.","journal-title":"IEEE Transactions on Intelligent Transportation Systems."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20185320"},{"key":"e_1_2_1_6_1","unstructured":"OpenPCDet Development Team. 2020. OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds. https:\/\/ github.com\/open-mmlab\/OpenPCDet.  OpenPCDet Development Team. 2020. OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds. https:\/\/ github.com\/open-mmlab\/OpenPCDet."},{"key":"e_1_2_1_7_1","first-page":"1","article-title":"Linear least-squares optimization for point-to-plane icp surface registration","volume":"4","author":"Low Kok-Lim","year":"2004","unstructured":"Kok-Lim Low . 2004 . Linear least-squares optimization for point-to-plane icp surface registration . Chapel Hill, University of North Carolina 4 , 10, 1 -- 3 . Kok-Lim Low. 2004. Linear least-squares optimization for point-to-plane icp surface registration. Chapel Hill, University of North Carolina 4, 10, 1--3.","journal-title":"Chapel Hill, University of North Carolina"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01135"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9560835"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341176"}],"container-title":["GetMobile: Mobile Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3599184.3599193","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3599184.3599193","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:38:04Z","timestamp":1750178284000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3599184.3599193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,17]]},"references-count":10,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,5,17]]}},"alternative-id":["10.1145\/3599184.3599193"],"URL":"https:\/\/doi.org\/10.1145\/3599184.3599193","relation":{},"ISSN":["2375-0529","2375-0537"],"issn-type":[{"value":"2375-0529","type":"print"},{"value":"2375-0537","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,17]]},"assertion":[{"value":"2023-05-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}