虚拟道路，更智能的安全：面向混合自动驾驶交通安全性分析的数字孪生框架
Virtual Roads, Smarter Safety: A Digital Twin Framework for Mixed Autonomous Traffic Safety Analysis
AI总结：
提出一个集成无人机激光雷达、CARLA、SUMO和PhysX的数字孪生平台，用于高保真模拟混合交通中自动驾驶与传统车辆的交互，以增强主动安全分析。
摘要：
本文提出了一个用于混合交通环境中主动安全分析的数字孪生平台。该平台基于多模态数据驱动的交通环境构建，数据来源于无人机机载激光雷达、OpenStreetMap以及车辆传感器数据（如GPS和倾角仪读数）。通过AI驱动的语义分割和地理配准技术，从机载激光雷达数据中生成高分辨率的三维道路几何。为模拟真实驾驶场景，该平台集成了CARLA（自动驾驶学习行动）模拟器、SUMO（城市交通仿真）交通模型以及NVIDIA PhysX车辆动力学引擎。CARLA提供详细的微观级传感器和感知数据，而SUMO管理宏观级交通流。NVIDIA PhysX能够精确建模不同条件下的车辆行为，考虑质量分布、轮胎摩擦力和质心等因素。该集成系统支持高保真仿真，可捕捉自动驾驶车辆与传统车辆之间的复杂交互。实验结果表明，该平台能够再现真实的车辆动力学和交通场景，增强了对主动安全措施的分析能力。总体而言，所提出的框架通过支持在动态和异构交通环境中对车辆行为进行深入的、基于物理的评估，推进了交通安全研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
