A Deployed Hybrid Vehicle-in-the-Loop Platform for Validating Cooperative Perception

• URL: http://arxiv.org/abs/2607.13806v1
• Date: Wed, 15 Jul 2026 13:11:17 GMT
• Title: A Deployed Hybrid Vehicle-in-the-Loop Platform for Validating Cooperative Perception
• Abstract summary: Vehicle-in-the-Loop (ViL) platform couples a real instrumented vehicle with a CARLA-based digital twin (DT)<n>We report its first integrated operation on a public-road-representative test track.
• Score: 31.7846414583602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: European safety regulation now permits a large share of automated-driving homologation evidence to be produced virtually, provided a validated physical-virtual facility generates it. We present a deployed hybrid Vehicle-in-the-Loop (ViL) platform that couples a real instrumented vehicle with a CARLA-based digital twin (DT) through a V2X message pipeline, and we report its first integrated operation on a public-road-representative test track. A real vehicle streams ETSI-compliant CAM/CPM messages into the DT, where a GPU-accelerated Cooperative Perception (CP) module fuses them into a probabilistic occupancy grid during scenario runtime. We demonstrate the platform on a multi-vehicle double T-intersection scenario, characterise the CP workload across nominal, rain and night conditions and five localization-noise levels, and discuss the platform's current architectural limits and the engineering targets they define. The results show that CP substantially widens field-of-view (FoV) coverage and improves occupied-cell recall, and that beyond a moderate localization-noise threshold, positioning uncertainty, and not weather, becomes the dominant error source. We outline the platform's trajectory toward a Mediterranean operational design domain (ODD) testing service.

Related papers

• VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation [50.18051979020699]
  VIPS is a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation.<n>CoS-V2X is a cooperative planning framework based on sparse representations.
  arXiv  Detail & Related papers  (2026-09-02T11:32:52Z)
• End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation [59.188006170485046]
  E2E-CDiff is an end-to-end conditional diffusion framework for controllable and realistic scenario generation.<n>Differentiable guidance regulates speed, enforces drivable-area compliance, and supports collision-avoidance or collision-seeking behaviors.<n>E2E-CDiff also performs competitively as a learning-based ego planner.
  arXiv  Detail & Related papers  (2026-07-21T02:13:25Z)
• Beyond Line of Sight: Hybrid Validation of V2X Collective Perception in Complex Scenarios [0.6065935686676507]
  This paper introduces a probabilistic framework and validation methodology for V2X-enabled Collective Perception (CP) in complex traffic scenarios.<n>The proposed algorithm extends the perceptual horizon of connected and autonomous vehicles by integrating heterogeneous sensor observations from multiple agents into a shared probabilistic occupancy grid.<n>Results show a 260 percent 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.
  arXiv  Detail & Related papers  (2026-07-01T12:38:01Z)
• CADET: A Modular Platform for Evaluating Distributed Cooperative Autonomy in Connected Autonomous Vehicles [3.544599494530976]
  CADET is a modular platform for systematic and reproducible evaluation of distributed cooperative autonomy systems.<n>We show that distributed deployment choices shape safety, with V2V intent packets outperforming cloud-based perception and RSU-assisted perception sustaining safety until overloaded by concurrent requests.
  arXiv  Detail & Related papers  (2026-06-02T15:13:13Z)
• Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation [8.373850248021927]
  This paper proposes a novel real-time hybrid digital twin platform for connected and automated vehicles (CAVs) validation.<n>Its core innovation lies in the tight coupling of a high-fidelity CARLA-SUMO co-simulation with a physical test site and vehicle via a low-latency Vehicle-to-Everything (V2X) communication link.<n> Detailed implementation includes using photogrammetry for full-scale asset reconstruction and a cloud-edge collaborative architecture for scalable, multi-user operation.
  arXiv  Detail & Related papers  (2026-05-19T07:44:59Z)
• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner [69.43049144653882]
  This paper introduces a unified architecture that decouples high-level decision-making from vehicle-specific control.<n>The proposed architecture is realized through GuadalPlanner, which defines standardized interfaces between planning, sensing, and vehicle execution.
  arXiv  Detail & Related papers  (2026-02-11T10:02:31Z)
• Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection [0.6999740786886536]
  Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations.<n>Traditional misbehaviour detection approaches, which rely on plausibility checks and statistical methods, suffer from high False Positive (FP) rates.<n>We present Attention In Motion (AIMformer), a transformer-based framework specifically tailored for real-time misbehaviour detection in vehicular platoons.
  arXiv  Detail & Related papers  (2025-12-17T14:45:33Z)
• Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework [62.47416496137193]
  We propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop.<n>The architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime.
  arXiv  Detail & Related papers  (2025-03-06T07:36:06Z)
• Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective [11.405406875019175]
  Collaborative Perception can be applied to broaden the field of view of connected vehicles. We propose a BEV feature-based CP network architecture for infrastructure-assisted AVP systems.
  arXiv  Detail & Related papers  (2024-03-22T12:11:06Z)
• Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
  We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view. Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
  arXiv  Detail & Related papers  (2022-11-15T13:52:41Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.