发表机构
HSE University（高等经济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对联网自动驾驶车辆协同驾驶仿真的保真度问题，在CAVIISE中提出统一工作流，结合三维射线追踪与自主交叉口管理模型，证实平面简化会扭曲信号损耗，强调需三维通信建模保障评估有效性。
AI 中文摘要
联网自动驾驶车辆的端到端仿真需要在移动性、环境建模、V2X无线电传播以及决策/控制模块间保持一致的保真度。然而，复杂道路基础设施的二维表示往往无法捕捉关键的信号传播动态，导致连通性假设过于乐观。本文在CAVIISE中提出了一个统一工作流，将基于地图的场景准备与微观移动性仿真相结合。该框架包含对三维中轨迹驱动传播与简化二维基线的对比分析，以及用于集成自主交叉口管理（AIM）模型的模块化接口。这些功能共同支持高保真的协同驾驶自动化（CDA）实验。利用Sionna RT在5.9 GHz下进行射线追踪，且两种几何变体采用相同的无线电和求解器配置，结果显示，多层道路基础设施的平面简化会消除物理存在的遮挡，严重扭曲信号损耗动态。在一座桥梁立交桥的案例研究中，二维基线消除了三维场景中观测到的遮挡区间，使中断行为从间歇性变为持续连通，平均信号损耗偏移约10 dB。这些由传播引发的偏差凸显了平面模型无法捕捉垂直遮挡的问题，因此需要基于三维的通信建模，以确保协同驾驶自动化和交叉口控制评估的有效性。
英文摘要
End-to-end simulation of connected and automated vehicles requires consistent fidelity across mobility, environment modeling, V2X radio propagation, and decision-making/control modules. However, 2D representations of complex road infrastructure often fail to capture critical signal propagation dynamics, leading to overly optimistic connectivity assumptions. This paper presents a unified workflow within CAVISE that integrates map-based scene preparation with microscopic mobility simulation. The proposed framework incorporates a comparative analysis of trace-driven propagation in 3D versus a simplified 2D baseline, and a modular interface for integrating Autonomous Intersection Management (AIM) models. Collectively, these capabilities enable high-fidelity Cooperative Driving Automation (CDA) experiments. Leveraging ray tracing using Sionna RT at 5.9 GHz with the same radio and solver configuration for both geometry variants, we show that planar reduction in multi-level road infrastructure can remove physically present occlusions and substantially distort signal-loss dynamics. In a bridge overpass case study, the 2D baseline eliminates an occlusion interval observed in 3D, changing the outage behavior from intermittent to consistently connected and yielding an average signal-loss shift on the order of 10 dB. These propagation-induced biases highlight the inability of planar models to capture vertical occlusions, necessitating 3D-aware communication modeling to ensure the validity of cooperative driving automation and intersection control evaluations.
Comments6 pages, 5 figures. Published in the 2026 International Russian Smart Industry Conference (SmartIndustryCon)
Journal ref2026 International Russian Smart Industry Conference (SmartIndustryCon), 2026
DOI：10.1109/SmartIndustryCon68821.2026.11493031
