  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.
