[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82176-en":3,"doc-seo-82176-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82176,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Latency Aware Digital Twin Assisted Cooperative Perception for Autonomous Vehicles","This paper proposes a digital-twin (DT)-assisted cooperative perception framework for autonomous vehicles to enhance perception accuracy under end-to-end (E2E) latency constraints while balancing accuracy and E2E latency under communication resource limitations. A perception-accuracy maximization problem is formulated with explicit latency and communication constraints and solved using a newly proposed coarse-to-fine search (CTFS) algorithm. Simulations report 96.6% accuracy close to exhaustive search and about 85.78% reduced computational complexity. DT-assisted operation also cuts non-DT communication cost by 50% via estimated time-synchronized state updates.","Latency-Aware Digital Twin-Assisted Cooperative Perception for Autonomous Vehicles  \nBoniface Uwizeyimana∗ , Manobendu Sarker†, and Abraham O. Fapojuwo∗  \n∗ Department of Electrical and Software Engineering, University of Calgary, Canada  \n†Poly-Grames Research Center, Department of Electrical Engineering, Polytechnique Montral, Canada  \n∗ {boniface.uwizeyimana, fapojuwo}@ucalgary.ca,†[manobendu.sarker@polymtl.ca](manobendu.sarker@polymtl.ca)  \narXiv :2607 .09070v1 [ ee ss . SY] 10 Jul 2026  \nAbstract—This paper introduces a digital-twin (DT)-assisted cooperative perception framework designed to improve perception accuracy under end-to-end (E2E) latency constraints and to balance perception accuracy and E2E latency under communication resource constraints in autonomous vehicles. We formulate an optimization problem that maximizes perception accuracy subject to latency and communication limitations, and solve it using a newly proposed coarse-to-fine search (CTFS) algorithm. Simulation results show that the proposed CTFS algorithm achieves 96.6% perception accuracy, close to exhaustive search, under latency constraints while reducing computational complexity by approximately 85.78% . The DT-assisted framework further achieves a 50% reduction in the non-DT communication cost through estimated, time-synchronized state updates.  \nIndex Terms—Cooperative perception, autonomous vehicles, perception accuracy, latency, digital twin.  \nI. INTRODUCTION  \nRecent advancements in autonomous vehicles have brought cooperative perception to the forefront, as it enhances sensing capabilities by mitigating occlusions, improving robustness under adverse conditions, and extending the perception range [1] . Autonomous vehicles rely on onboard sensors such as light detection and ranging (LiDAR) and cameras to perceive their surroundings and support safe navigation. However, individual vehicles face inherent limitations due to restricted sensing ranges and environmental obstructions. Cooperative perception addresses these limitations by enabling vehicles to share sensory information, which is then fused by an aggregating agent to improve detection performance. Edgeassisted cooperative perception [2] further strengthens this capability by combining local observations with information shared by neighbouring vehicles, thereby enabling a more comprehensive environmental understanding. Achieving high perception accuracy is essential, but it must also satisfy strict real-time constraints to support safe and reliable operation. In this context, latency plays a critical role in determining the practical effectiveness of cooperative perception systems.  \nExisting approaches to cooperative perception generally operate at three levels of fusion: early, intermediate, and late fusion [3], [4] . Early fusion involves transmitting raw sensor data to an aggregating agent, achieving high detection accuracy [4], but facing significant communication overhead and sensitivity to latency and noise, which limits its practical deployment [1] . Intermediate fusion allows vehicles to extract local features and send only feature maps, balancing accuracy  \nand communication costs while offering improved robustness to latency. Late fusion shares detection results instead of raw data, reducing communication needs but typically resulting in lower accuracy due to limited use of sensory information [4] .  \nDespite these developments, the problem of maximizing perception accuracy under strict latency constraints remains insufficiently explored. Prior work in [5] introduced a communication-aware strategy that selects the spatial area of interest for transmission using a communication confidence threshold, thereby reducing communication overhead. This approach is further evaluated in [4] using the UrbanIng dataset. Nevertheless, determining the optimal transmission strategy in real time remains challenging when latency constraints and resource limitations must be jointly satisfied. 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it also reduces non-DT communication cost by 50% through estimated synchronized updates.\"}]",1784178603,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"latency-aware-digital-twin-assisted-cooperative-perception-for-autonomous-vehicles","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/latency-aware-digital-twin-assisted-cooperative-perception-for-autonomous-vehicles/82176/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the proposed DT-assisted cooperative perception framework?","Question",{"text":75,"@type":76},"To improve perception accuracy for autonomous vehicles under end-to-end latency constraints while jointly balancing perception accuracy and latency given communication resource limits.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper optimize perception accuracy under latency and communication constraints?",{"text":80,"@type":76},"It formulates an optimization problem that maximizes perception accuracy subject to latency and communication limitations, then solves it with a coarse-to-fine search (CTFS) algorithm.",{"name":82,"@type":73,"acceptedAnswer":83},"What benefits does the digital twin (DT) layer provide in the framework?",{"text":84,"@type":76},"DT enables time-synchronized virtual replicas of vehicles and RSUs, supports anticipation of short-term network states from historical and real-time data, and allows latency-aware adaptation at edge servers; 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