Simulation is a fundamental tool in developing autonomous vehicles, enabling rigorous testing without the logistical and safety challenges associated with real-world trials. As autonomous vehicle technologies evolve and public safety demands increase, advanced, realistic simulation frameworks are critical. Current testing paradigms employ a mix of general-purpose and specialized simulators, such as CARLA and IVRESS, to achieve high-fidelity results. However, these tools often struggle with compatibility due to differing platform, hardware, and software requirements, severely hampering their combined effectiveness. This paper introduces BlueICE, an advanced framework for ultra-realistic simulation and digital twinning, to address these challenges. BlueICE's innovative architecture allows for the decoupling of computing platforms, hardware, and software dependencies while offering researchers customizable testing environments to meet diverse fidelity needs. Key features include containerization to ensure compatibility across different systems, a unified communication bridge for seamless integration of various simulation tools, and synchronized orchestration of input and output across simulators. This framework facilitates the development of sophisticated digital twins for autonomous vehicle testing and sets a new standard in simulation accuracy and flexibility. The paper further explores the application of BlueICE in two distinct case studies: the ICAT indoor testbed and the STAR campus outdoor testbed at the University of Delaware. These case studies demonstrate BlueICE's capability to create sophisticated digital twins for autonomous vehicle testing and underline its potential as a standardized testbed for future autonomous driving technologies.

翻译：仿真是自动驾驶车辆开发中的基础工具，能够在无需实地测试的物流和安全挑战下进行严格验证。随着自动驾驶技术的演进及公众安全需求的提升，先进且逼真的仿真框架变得至关重要。当前的测试范式采用通用与专用模拟器（如CARLA和IVRESS）相结合的方式，以实现高保真结果。然而，这些工具常因平台、硬件和软件需求差异而出现兼容性问题，严重削弱了其综合效能。本文提出BlueICE——一种面向超逼真仿真与数字孪生的先进框架，以应对上述挑战。BlueICE的创新架构允许将计算平台、硬件和软件依赖分离，同时为研究人员提供可定制的测试环境以满足多样化的保真需求。其关键特性包括：通过容器化确保跨系统兼容性、构建统一通信桥梁实现多种仿真工具的无缝集成，以及跨模拟器的输入/输出同步编排。该框架促进了面向自动驾驶车辆测试的复杂数字孪生开发，并树立了仿真精度与灵活性的新标杆。本文进一步通过两个案例展示BlueICE的应用：特拉华大学ICAT室内测试平台与STAR园区室外测试平台。这些案例证明了BlueICE在创建面向自动驾驶车辆测试的复杂数字孪生方面的能力，并凸显了其作为未来自动驾驶技术标准化测试平台的潜力。