While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that, due to the complexity of autonomous software stacks and public road traffic, the optimal validation of entire stacks is an open research problem. Our paper targets these two aspects. We present our autonomous research vehicle EDGAR and its digital twin, a detailed virtual duplication of the vehicle. While the vehicle's setup is closely related to the state of the art, its virtual duplication is a valuable contribution as it is crucial for a consistent validation process from simulation to real-world tests. In addition, different development teams can work with the same model, making integration and testing of the software stacks much easier, significantly accelerating the development process. The real and virtual vehicles are embedded in a comprehensive development environment, which is also introduced. All parameters of the digital twin are provided open-source at https://github.com/TUMFTM/edgar_digital_twin.

翻译：当前自动驾驶的研究与开发主要集中于新功能和新算法的开发，而将孤立软件组件整合为完整软件栈的过程却鲜有涉及。此外，由于自动驾驶软件栈及公共道路交通的复杂性，如何最优地验证整个软件栈仍是一个开放的研究问题。本文针对这两个方面展开研究。我们介绍了自动驾驶研究车辆EDGAR及其数字孪生体——该车辆的详细虚拟副本。虽然车辆本身配置与当前技术水平紧密相关，但其虚拟副本作为关键要素，对于实现从仿真到实际测试的一致性验证流程具有重要贡献。此外，不同开发团队可使用同一模型进行协作，这极大地简化了软件栈的集成与测试，从而显著加速开发进程。真实车辆与虚拟车辆均嵌入于一个综合开发环境中，本文亦对此环境进行了介绍。数字孪生体的所有参数已开源发布，详见https://github.com/TUMFTM/edgar_digital_twin。