一种用于验证协同感知的已部署混合车辆在环平台
A Deployed Hybrid Vehicle-in-the-Loop Platform for Validating Cooperative Perception
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中文总结 AI 辅助
该研究提出一种通过V2X消息管道耦合真实车辆与数字孪生的混合车辆在环平台，用于验证协同感知。在多车辆场景测试，表征不同条件下CP工作负载，结果显示CP能扩视野、提召回率，定位不确定性在一定阈值后成主要误差源，还规划了平台向地中海ODD测试服务发展轨迹。
中文摘要 AI 辅助
欧洲安全法规允许通过经过验证的物理-虚拟设施虚拟生成大部分自动驾驶认证证据。我们展示了一个已部署的混合车辆在环（ViL）平台，它通过V2X消息管道将真实的仪器车辆与基于CARLA的数字孪生（DT）耦合。在公共道路代表性测试轨道上进行了首次集成操作。真实车辆将符合ETSI的CAM/CPM消息传输到DT，GPU加速的协同感知（CP）模块在场景运行时将其融合到概率占用网格中。在多车辆双T型交叉路口场景中演示了该平台，表征了不同条件下的CP工作负载，并讨论了平台的架构限制和工程目标。结果表明CP扩大了视野覆盖范围并提高了占用单元召回率，超过适度定位噪声阈值后，定位不确定性而非天气成为主要误差源。我们概述了该平台向地中海运行设计域（ODD）测试服务发展的轨迹。
英文摘要
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.
