搭建真实车与虚拟车协同验证平台，提升自动驾驶感知可靠性。
A Deployed Hybrid Vehicle-in-the-Loop Platform for Validating Cooperative Perception
- 真实车辆与CARLA虚拟孪生通过车联网通信联动，实现协同感知融合。
- 在雨夜等复杂条件下，协同感知使视野覆盖提升，误检率下降。
- 定位精度超过阈值后，误差主要来自位置不确定性而非天气影响。
欧洲安全法规现已允许以虚拟方式生成大量自动驾驶认证证据，前提是使用经验证的软硬件混合设施。本文介绍一个已部署的混合车辆在环（ViL）平台，该平台通过V2X消息管道将真实仪器化车辆与基于CARLA的数字孪生（DT）连接，在代表公共道路的测试场完成首次集成运行。真实车辆实时发送符合ETSI标准的CAM/CPM消息至数字孪生系统，其中基于GPU加速的协同感知（CP）模块在场景运行期间将这些信息融合为概率占用网格。我们在多车双T形交叉口场景中验证了该平台，评估了正常、雨天和夜间条件下及五种定位噪声水平下的CP负载，并讨论了当前架构的局限性及其对工程优化目标的定义。结果表明，协同感知显著扩大了视场覆盖范围并提升了占位单元召回率；当定位噪声超过一定阈值后，位置不确定性成为主导误差来源，而非天气因素。论文还规划了该平台向地中海运营设计域（ODD）测试服务演进的路径。
原文摘要 · Abstract (English)
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.
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