  Modeling and simulation of autonomous vehicles plays a crucial role in
achieving enterprise-scale realization that aligns with technical, business and
regulatory requirements. Contemporary trends in digital lifecycle treatment
have proven beneficial to support SBD as well as V&V of these complex systems.
Although, the development of appropriate fidelity simulation models capable of
capturing the intricate real-world physics and graphics (real2sim), while
enabling real-time interactivity for decision-making, has remained a challenge.
Nevertheless, recent advances in AI-based tools and workflows, such as online
deep-learning algorithms leveraging live-streaming data sources, offer the
tantalizing potential for real-time system-identification and adaptive modeling
to simulate vehicles, environments, as well as their interactions. This
transition from virtual prototypes to digital twins not only improves
simulation fidelity and real-time factor, but can also support the development
of online adaption/augmentation techniques that can help bridge the gap between
simulation and reality (sim2real). In such a milieu, this work focuses on
developing autonomy-oriented digital twins of vehicles across different scales
and configurations to help support the streamlined development and deployment
of Autoware stack, using a unified real2sim2real toolchain. Particularly, the
core deliverable for this project was to integrate the Autoware stack with
AutoDRIVE Ecosystem to demonstrate end-to-end task of map-based autonomous
navigation. This work discusses the development of vehicle and environment
digital twins using AutoDRIVE Ecosystem, along with various APIs and HMIs to
connect with the same, followed by a detailed section on AutoDRIVE-Autoware
integration. Furthermore, this study describes the first-ever off-road
deployment of the Autoware stack, expanding the ODD beyond on-road autonomous
navigation.
