Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the integration of real-world data into simulation environments becomes increasingly critical. Among various technologies utilized by CAVs, Vehicle-to-Everything (V2X) communication plays a crucial role in ensuring a seamless transmission of information between CAVs, infrastructure, and other road users. However, most existing studies have focused on developing and testing communication protocols, resource allocation strategies, and data dissemination techniques in V2X. There is a gap where real-world V2X data is integrated into simulations to generate diverse and high-fidelity traffic scenarios. To fulfill this research gap, we leverage real-world Signal Phase and Timing (SPaT) data from Roadside Units (RSUs) to enhance the fidelity of CAV simulations. Moreover, we developed an algorithm that enables Autonomous Vehicles (AVs) to respond dynamically to real-time traffic signal data, simulating realistic V2X communication scenarios. Such high-fidelity simulation environments can generate multimodal data, including trajectory, semantic camera, depth camera, and bird's eye view data for various traffic scenarios. The generated scenarios and data provide invaluable insights into AVs' interactions with traffic infrastructure and other road users. This work aims to bridge the gap between theoretical research and practical deployment of CAVs, facilitating the development of smarter and safer transportation systems.

翻译：仿真是确保联网与自动驾驶汽车（CAV）测试验证过程准确、高效且贴近现实的关键环节。随着CAV的加速普及，将真实世界数据融入仿真环境变得日益重要。在CAV采用的众多技术中，车联网（V2X）通信对于保障CAV、基础设施及其他道路使用者之间信息的无缝传输起着至关重要的作用。然而，现有研究大多聚焦于V2X通信协议的开发测试、资源分配策略及数据分发技术，而在将真实世界V2X数据整合至仿真以生成多样化高保真交通场景方面仍存在空白。为填补这一研究缺口，本研究利用来自路侧单元（RSU）的真实世界信号相位与配时（SPaT）数据来提升CAV仿真的真实度。此外，我们开发了一种算法，使自动驾驶汽车（AV）能够动态响应实时交通信号数据，从而模拟真实的V2X通信场景。此类高保真仿真环境可生成多模态数据，包括针对各类交通场景的轨迹数据、语义摄像头数据、深度摄像头数据及鸟瞰视图数据。生成的场景与数据为理解自动驾驶汽车与交通基础设施及其他道路使用者的交互提供了宝贵洞见。本工作旨在弥合CAV理论研究与实际部署之间的鸿沟，助力构建更智能、更安全的交通系统。