构建数字孪生平台，模拟自动驾驶与人工驾驶混行交通的安全性。
Virtual Roads, Smarter Safety: A Digital Twin Framework for Mixed Autonomous Traffic Safety Analysis
- 融合无人机激光雷达、地图数据与车辆传感器，生成高精度3D道路模型。
- 集成CARLA、SUMO与NVIDIA PhysX，实现车辆动力学与交通流的高保真仿真。
- 适合自动驾驶安全评估、智慧交通系统研发人员使用。
本文提出一种用于混合交通环境下主动安全分析的数字孪生平台。该平台基于无人机航拍激光雷达、OpenStreetMap及车辆传感器数据（如GPS和倾角计读数）构建多模态交通环境。通过人工智能驱动的语义分割与地理配准技术，生成高分辨率3D道路几何结构。为模拟真实驾驶场景，平台整合了CAR Learning to Act（CARLA）仿真器、交通流仿真工具Simulation of Urban MObility（SUMO）以及NVIDIA PhysX车辆动力学引擎。CARLA提供微观层面的传感器与感知数据，SUMO负责宏观交通流管理，NVIDIA PhysX则精准建模车辆在不同条件下的行为，涵盖质量分布、轮胎摩擦力与质心位置。该集成系统支持高保真仿真，捕捉自动驾驶与传统车辆间的复杂交互。实验结果表明，平台能够重现真实的车辆动力学与交通场景，显著提升主动安全措施的分析能力。总体而言，该框架通过物理信息驱动的深度评估，推动了动态异构交通环境中车辆行为研究的发展。
原文摘要 · Abstract (English)
This paper presents a digital-twin platform for active safety analysis in mixed traffic environments. The platform is built using a multi-modal data-enabled traffic environment constructed from drone-based aerial LiDAR, OpenStreetMap, and vehicle sensor data (e.g., GPS and inclinometer readings). High-resolution 3D road geometries are generated through AI-powered semantic segmentation and georeferencing of aerial LiDAR data. To simulate real-world driving scenarios, the platform integrates the CAR Learning to Act (CARLA) simulator, Simulation of Urban MObility (SUMO) traffic model, and NVIDIA PhysX vehicle dynamics engine. CARLA provides detailed micro-level sensor and perception data, while SUMO manages macro-level traffic flow. NVIDIA PhysX enables accurate modeling of vehicle behaviors under diverse conditions, accounting for mass distribution, tire friction, and center of mass. This integrated system supports high-fidelity simulations that capture the complex interactions between autonomous and conventional vehicles. Experimental results demonstrate the platform's ability to reproduce realistic vehicle dynamics and traffic scenarios, enhancing the analysis of active safety measures. Overall, the proposed framework advances traffic safety research by enabling in-depth, physics-informed evaluation of vehicle behavior in dynamic and heterogeneous traffic environments.
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