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：在不同空间尺度的自主车辆平台中，受空间、安全及成本因素制约，研究与应用领域需采用相应的开发方案。然而，跨操作尺度的自主算法部署与验证面临诸多挑战，包括尺度特异性动力学特性、传感器集成复杂性、计算资源限制、法规合规要求、环境多变性、与其他交通参与者的交互以及可扩展性问题。在此背景下，本研究致力于构建统一框架，对跨不同尺度及运行设计域（ODD）的自主车辆平台进行数字孪生建模与仿真，以支持自主软件栈的协同开发与验证。具体而言，本文讨论了4种自主地面车辆（覆盖3种尺度并针对3种不同ODD）的数字孪生表征开发。我们研究了采用这些面向自主性的数字孪生部署通用自主软件栈，以实现基于端到端地图导航的ODD特定目标。最后，本文还探讨了所提框架的灵活性——支持虚拟、混合及物理测试场景，并实现无缝的仿真到现实迁移。