[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82480-en":3,"doc-seo-82480-105":28,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},82480,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782109480056885918",8,"Research & Report","HydraCollab Adaptive Collaborative Perception for Distributed Autonomous Systems","Collaborative-perception helps multi-robot systems improve situational awareness by exchanging perceptual information, but existing approaches face a trade-off between communication bandwidth and perception accuracy. Real-world networks constrain bandwidth, so minimizing communication overhead without losing perception performance is essential. HydraCollab introduces an adaptive framework that selectively transmits the most informative sensor features and switches collaboration type using spatial confidence maps. Experiments on V2X-R, V2X-Radar, and UAV3D-mini show the best accuracy–cost trade-off among prior methods, reducing bandwidth while slightly improving accuracy.","HydraCollab: Adaptive Collaborative-Perception for Distributed  \nAutonomous Systems  \nLuke Chen Cheng-Ju Wu David R. Martin∗ Qilin Ye Pramod Khargonekar Mohammad Abdullah Al Faruque  \narXiv :2607 .00191v1 [ cs .RO] 30 Jun 2026  \nAbstract—Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative-perception systems face an inherent trade-off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better perception results at the cost of increased communication overhead. However, realworld communication networks impose bandwidth constraints that require minimizing communication overhead without sacrificing perception performance. To address this challenge, we propose HydraCollab, an adaptive collaborative-perception framework that (i) selectively transmits the most informative sensor features and (ii) dynamically employs collaboration strategies (intermediate or late) based on spatial confidence maps. Extensive evaluations on the V2X-R, V2X-Radar and UAV3D-mini datasets demonstrate that HydraCollab achieves the best overall trade-off between accuracy and communication cost among existing collaborative-perception methods. Relative to SOTA Where2comm, HydraCollab uses only 41% of the bandwidth on V2X-R and 26% on V2X-Radar while improving performance by 0.78% and 0.75% respectively. Our code and models are available at [https://github.com/AICPS/HydraCollab](https://github.com/AICPS/HydraCollab).  \nI. INTRODUCTION  \nCollaborative-perception enhances performance in multirobot systems by enabling robots to communicate perceptual information, overcoming individual limitations such as occlusions and restricted sensor range, and thereby improving situational awareness [1] . Accurate perception is critical to ensure the safety of robotic systems such as autonomous vehicles [2], [3] and UAV swarms [4], where perceptual failures can lead to catastrophic results including collisionsand injuries. Often, robots may wish to exchange perceptual information with non-robotic sensing platforms, such as infrastructure-mounted sensors in autonomous driving scenarios. We therefore use the term “agent” to refer to each participant in collaborative-perception.  \nIn collaborative-perception, a fundamental trade-off exists between perception accuracy and communication bandwidth: sharing more information enhances performance but increases communication overhead. Given the limitations of real-world communication systems in supporting highbandwidth exchange in real time, a key challenge is to maximize perception gains while minimizing communication costs. This trade-off has motivated three primary collaboration strategies, distinguished by the type of information shared. Late collaboration transmits only final detection  \n*Corresponding Author. Department of Electrical Engineering and Computer Science, University of California, Irvine, USA. {panwangc, cwu30, davidrm3, qiliny3, pramod.khargonekar, [alfaruqu](alfaruqu}@uci.edu)[}](alfaruqu}@uci.edu)[@uci.edu](alfaruqu}@uci.edu).  \nFig. 1. LiDAR with noisy Radar fusion leads to suboptimal detections. Communicating only LiDAR information can improve both performance and communication bandwidth of the perception system (V2X-R dataset) .  \nresults, minimizing bandwidth but limiting collaboration to perception outputs [1], [5], [6] . Early collaboration exchanges raw sensor data, providing maximum information richness at the cost of high bandwidth requirements [1],[7] . Intermediate collaboration seeks a balance by sharing encoded feature representations, making it the dominant paradigm in many recent systems [8], [9], as it preserves informative content while significantly reducing communication overhead. Further advances reduce communication demands through selective strategies that determine what regions of interest to share [9], which agents to communicate with","cbCaimv4lSSy9xNM","https://ap.wps.com/l/cbCaimv4lSSy9xNM","pdf",15473061,1,"English","en",105,"# Introduction\n## Collaborative-perception trade-off\n## Collaboration strategies (late, early, intermediate)\n## Limitations of existing methods\n## Motivation for selective feature sharing\n## Motivation for hybrid collaboration via spatial confidence","[{\"question\":\"What trade-off does HydraCollab address in collaborative perception?\",\"answer\":\"It targets the trade-off between perception accuracy and communication bandwidth, where sending more information improves perception but increases communication overhead.\"},{\"question\":\"How does HydraCollab reduce communication cost while keeping perception performance?\",\"answer\":\"It selectively transmits the most informative sensor features and dynamically selects intermediate or late collaboration based on spatial confidence maps.\"},{\"question\":\"What results does HydraCollab achieve on benchmark datasets?\",\"answer\":\"On V2X-R and V2X-Radar, HydraCollab uses only 41% and 26% of the bandwidth of Where2comm while improving performance by 0.78% and 0.75% respectively.\"}]",1784180825,20,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":26},"hydracollab-adaptive-collaborative-perception-for-distributed-autonomous-systems","",{"@graph":34,"@context":84},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/hydracollab-adaptive-collaborative-perception-for-distributed-autonomous-systems/82480/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What trade-off does HydraCollab address in collaborative perception?","Question",{"text":74,"@type":75},"It targets the trade-off between perception accuracy and communication bandwidth, where sending more information improves perception but increases communication overhead.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does HydraCollab reduce communication cost while keeping perception performance?",{"text":79,"@type":75},"It selectively transmits the most informative sensor features and dynamically selects intermediate or late collaboration based on spatial confidence maps.",{"name":81,"@type":72,"acceptedAnswer":82},"What results does HydraCollab achieve on benchmark datasets?",{"text":83,"@type":75},"On V2X-R and V2X-Radar, HydraCollab uses only 41% and 26% of the bandwidth of Where2comm while improving performance by 0.78% and 0.75% 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