Abstract
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.
Abstract (translated)
模拟是在发展自动驾驶车辆中的一种基本工具，它允许在不需要与现实世界试验相关的物流和安全性挑战的情况下进行严格的测试。随着自动驾驶技术的发展和公共安全需求的增长，先进的、逼真的模拟框架至关重要。当前的测试范式采用通用和专用模拟器，如CARLA和IVRESS，以实现高保真度的结果。然而，由于不同平台、硬件和软件需求的不同，这些工具往往难以兼容，严重地阻碍了它们的综合效果。本文介绍了一种名为BlueICE的高级框架，以解决这些挑战。BlueICE创新的设计允许在计算平台、硬件和软件依赖之间进行解耦，并为研究人员提供可定制的测试环境，以满足不同的保真度需求。关键特点包括容器化以确保不同系统之间的兼容性，统一的通信桥实现各种模拟工具的无缝集成，以及模拟器之间同步操作输入和输出。这个框架促进了自动驾驶车辆测试中复杂数字孪生的开发，为模拟准确性和灵活性设定了新的标准。本文进一步探讨了BlueICE在两个不同案例研究中的应用：美国马里兰大学ICAT室内测试区和大学 of Delaware的STAR校园户外测试区。这些案例研究展示了BlueICE在创建自动驾驶车辆测试中的复杂数字孪生方面的能力，并强调了其在未来自动驾驶技术标准化测试床上的潜力。
URL
https://arxiv.org/abs/2405.01328
