{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:42:32Z","timestamp":1773801752545,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Cooperative perception is critical for autonomous driving, overcoming the inherent limitations of a single vehicle, such as occlusions and constrained fields-of-view. However, current approaches sharing dense Bird's-Eye-View (BEV) features are constrained by quadratically-scaling communication costs and the lack of flexibility and interpretability for precise alignment across asynchronous or disparate viewpoints. While emerging sparse query-based methods offer an alternative, they often suffer from inadequate geometric representations, suboptimal fusion strategies, and training instability. In this paper, we propose SparseCoop, a fully sparse cooperative perception framework for 3D detection and tracking that completely discards intermediate BEV representations. Our framework features a trio of innovations: a kinematic grounded instance query that uses an explicit state vector with 3D geometry and velocity for precise spatio-temporal alignment; a coarse-to-fine aggregation module that effectively integrates information from both matched and unmatched instances; and a cooperative instance denoising task that provides stable, abundant supervision to accelerate and stabilize training. Experiments on V2X-Seq and Griffin datasets show SparseCoop achieves state-of-the-art performance. Notably, it delivers this performance with superior computational efficiency and a highly competitive transmission cost, while showing remarkable robustness to real-world challenges like communication latency.<\/jats:p>","DOI":"10.1609\/aaai.v40i12.37952","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:55:53Z","timestamp":1773791753000},"page":"9876-9884","source":"Crossref","is-referenced-by-count":0,"title":["SparseCoop: Cooperative Perception with Kinematic-Grounded Queries"],"prefix":"10.1609","volume":"40","author":[{"given":"Jiahao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Zhongwei","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Wenchao","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Jiaru","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Haibao","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yuner","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chenyang","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Chuang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"He","sequence":"additional","affiliation":[]},{"given":"Shaobing","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jianqiang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37952\/41914","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37952\/41914","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:55:53Z","timestamp":1773791753000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i12.37952","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}