Autonomous vehicle platforms of varying spatial scales are employed within the research and development spectrum based on space, safety and monetary constraints. However, deploying and validating autonomy algorithms across varying operational scales presents challenges due to scale-specific dynamics, sensor integration complexities, computational constraints, regulatory considerations, environmental variability, interaction with other traffic participants and scalability concerns. In such a milieu, this work focuses on developing a unified framework for modeling and simulating digital twins of autonomous vehicle platforms across different scales and operational design domains (ODDs) to help support the streamlined development and validation of autonomy software stacks. Particularly, this work discusses the development of digital twin representations of 4 autonomous ground vehicles, which span across 3 different scales and target 3 distinct ODDs. We study the adoption of these autonomy-oriented digital twins to deploy a common autonomy software stack with an aim of end-to-end map-based navigation to achieve the ODD-specific objective(s) for each vehicle. Finally, we also discuss the flexibility of the proposed framework to support virtual, hybrid as well as physical testing with seamless sim2real transfer.

翻译：在研发过程中，根据空间、安全和成本约束，不同空间尺度的自主驾驶车辆平台被广泛采用。然而，由于尺度特定的动力学特性、传感器集成复杂性、计算约束、法规考量、环境变化、与其他交通参与者的交互以及可扩展性问题，在不同运行尺度上部署和验证自主算法面临着诸多挑战。在此背景下，本研究致力于开发一个统一框架，用于建模和模拟不同尺度及运行设计域下的自主驾驶车辆平台数字孪生，以支持自主软件栈的流线化开发与验证。具体而言，本文探讨了4种地面自主车辆的数字孪生表示构建，这些车辆跨越3种不同尺度并瞄准3个独特的ODD。我们研究了采用这些面向自主性的数字孪生来部署通用自主软件栈，以实现基于地图的端到端导航，从而达成每辆车特定的ODD目标。最后，我们还讨论了所提出框架在支持虚拟、混合及物理测试中的灵活性，并实现无缝的sim2real迁移。