[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83548-en":3,"doc-seo-83548-105":30,"detail-sidebar-cat-0-en-105":92},{"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},83548,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Beyond Line of Sight Hybrid Validation of V2X Collective Perception in Complex Scenarios","This paper presents a probabilistic framework and hybrid validation method for V2X-enabled Collective Perception (CP) in complex traffic settings. A Bayesian fusion algorithm integrates heterogeneous observations from multiple agents into a shared probabilistic occupancy grid, where each cell stores occupancy likelihood and uncertainty for explainable situational awareness beyond a single ego vehicle’s view. A hybrid testing framework bridges simulation and real-world trials by combining CARLA virtual environments with vehicle-in-the-loop experimentation, validated on a roundabout scenario.","Beyond Line of Sight: Hybrid Validation of V2X Collective Perception in Complex Scenarios  \nMarkos Antonopoulos, Anastasia Bolovinou, Bill Roungas, Elena Daskalaki, Angelos Amditis  \nInstitute of Communication and Computer Systems  \nAthens, Greece  \n{markos.antonopoulos, anastasia.bolovinou, vroungas, e.daskalaki, [a.amditis](a.amditis}@iccs.gr)[}](a.amditis}@iccs.gr)[@iccs.gr](a.amditis}@iccs.gr)  \narXiv :2607 .00874v2 [ cs .RO] 3 Jul 2026  \nAbstract—This paper introduces a probabilistic framework and hybrid validation methodology for V2X-enabled Collective Perception (CP) in complex traffic scenarios. The proposed Bayesian fusion algorithm extends the perceptual horizon of connected and autonomous vehicles by integrating heterogeneous sensor observations from multiple agents into a shared probabilistic occupancy grid. Each cell of this grid encapsulates both occupancy likelihood and uncertainty, enabling explainable and trustworthy situational awareness beyond the ego vehicle’s field of view. To bridge the gap between simulation and realworld evaluation, a hybrid testing framework is developed, combining CARLA-based virtual environments with vehicle-inthe-loop experimentation. Experimental results in a roundabout scenario demonstrate a 260% increase in field-of-view coverage and a rise in occupied-cell recall from 0.82 (ego-only) to 0.94 (six-agent CP) under nominal localization conditions. Overall, the proposed approach provides a reproducible and interpretable foundation for validating CP systems, supporting the safe and certifiable deployment of cooperative autonomous vehicles.  \nIndex Terms—Collective Perception (CP), Autonomous Vehicles (AVs), Cooperative Intelligent Transport Systems (C-ITS), V2X Communication, Bayesian Data Fusion, Occupancy Grid Mapping, Hybrid Testing, Scenario-Based Validation, CARLA Simulator, Roundabout Navigation.  \nI. INTRODUCTION  \nCollective Perception (CP), currently standardized by ETSI as a second generation V2X communication service, is especially promising for Autonomous Vehicles (AVs), as it allows connected AV agents “see through the eyes of others” who may share processed sensor data via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, extended field-of-view awareness, and redundancy. In this work, a safety validation proof-of-concept (PoC) for CP scenario-based testing for a roundabout navigation scenario is designed. The PoC includes both connected AVs and nonconnected vehicles, assuming connected virtual agents that can exchange ETSI-alike Collective Perception Message (CPM) information. Hence, the objective of this work is to (1) develop an algorithm for fusion of object information coming from multiple observers based on probabilistic scene state estimation via occupancy grid maps, (2) integrate data reliability  \nThis research has been conducted as part of the EVENTS project, which is funded by the European Union, under grant agreement No 101069614 . Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the granting authority can be held responsible for them.  \nmetrics enabling penalization of CP unreliable data and object associations’ conflict resolution, and (3) explore virtual and hybrid test environments for CP system performance evaluation focusing on augmented perception quality. It should be noted that for reasons of simplicity, no networking aspects are taken into account; ETSI-alike CP messages are assumed available with frequency/delay that can vary.  \nII. LITERATURE REVIEW  \nEarly CP research primarily focused on communication efficiency, including latency and congestion control, to maintain Quality of Service (QoS) . However, perception-centered research has reframed CP as a multi-sensor data fusion problem. Bayesian and probabilistic models now dominate the field due to their inherent expl","cbCaivg8s7MbVPT1","https://ap.wps.com/l/cbCaivg8s7MbVPT1","pdf",1236728,5,1,6,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"What problem does the paper address for V2X collective perception?\",\"answer\":\"It targets safety validation of collective perception in complex traffic scenarios by enabling probabilistic fusion of information from multiple observers, extending awareness beyond the ego vehicle’s field of view.\"},{\"question\":\"How does the proposed fusion approach represent uncertainty and occupancy?\",\"answer\":\"It uses a Bayesian fusion algorithm to build a shared probabilistic occupancy grid, where each cell contains both occupancy likelihood and uncertainty for explainable situational awareness.\"},{\"question\":\"How is the simulation-to-reality gap handled in the testing methodology?\",\"answer\":\"The paper introduces hybrid testing that combines CARLA-based virtual environments with vehicle-in-the-loop experiments, evaluated in a roundabout scenario under nominal localization 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