Autonomy Oriented Digital Twins for Real2Sim2Real Autoware Deployment (2402.14739v1)

Abstract: 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.

• The paper introduces a digital twin framework that enables seamless Real2Sim2Real transitions for Autoware deployment.
• It employs a modular AutoDRIVE Ecosystem to simulate accurate vehicle dynamics and sensor-actuator interactions across varied scales.
• Notably, the study demonstrates off-road Autoware applications, such as autonomous racing and valet parking, to enhance simulation fidelity.

Autonomy Oriented Digital Twins for Real2Sim2Real Autoware Deployment

The paper "Autonomy Oriented Digital Twins for Real2Sim2Real Autoware Deployment" presents an in-depth paper on leveraging digital twins to enhance autonomous vehicle modeling. The authors, Chinmay Vilas Samak and Tanmay Vilas Samak, elucidate a streamlined approach for deploying the Autoware stack, focusing on integrating digital twins with autonomous vehicle control systems using the AutoDRIVE Ecosystem. The paper significantly contributes to bridging the simulation-reality gap through enhanced fidelity in simulation models, fostering both simulation-based design (SBD) and verification and validation (V&V) methodologies.

Methodology and System Architecture

Central to this work is the development of digital twin models for various vehicle scales and environments. The research adopts the AutoDRIVE Simulator, grounded on modular object-oriented programming, to create realistic vehicle dynamics, sensors, and actuators. The paper describes simulations across small-scale (Nigel and F1TENTH), mid-scale (Hunter SE), and full-scale (OpenCAV) platforms, detailing their respective sensor suites and actuator dynamics. An emphasis is placed on the interoperability of these models across different operational design domains (ODDs), facilitating end-to-end autonomy deployment in diverse scenarios.

Further, a key deliverable involved the AutoDRIVE Ecosystem's API integration, supporting Python, C++, ROS, ROS 2, and Autoware frameworks. This integration allows the digital twins to interface effectively with various software stacks, enabling real-time system checks and adaptability. The work also integrates human-machine interfaces (HMIs), enhancing operational control over these digital twins and providing a foundation for a true digital twin framework.

Deployment and Results

The paper reports the deployment of the Autoware stack across small, mid, and full-scale autonomous vehicle models, focusing on tasks such as mapping unknown environments, recording trajectories, and autonomous trajectory tracking. Notably, the paper marks the first off-road deployment of the Autoware stack, advancing its operational domain beyond traditional on-road navigation. Each vehicle model's utility is demonstrated through a set of designed scenarios, notably autonomous racing for the F1TENTH and valet parking for Nigel and OpenCAV, showcasing the utility of digital twins in real2sim2real transitions.

Technical Implications and Future Work

Technically, the deployment of digital twins serves as an enabler for enhancing simulation fidelity, critical for developing robust autonomy systems. The transition from static virtual models to dynamic digital twins allows for the incorporation of real-world data, improving real-time decision-making capacity in autonomous vehicles. This evolution is vital for expanding the applicability of simulation tools in autonomous vehicle research, especially concerning novel systems like the Autoware stack.

Looking forward, the research suggests avenues such as integrating dynamic replanning capabilities and enhancing real2sim2real transitions through multi-agent systems. These developments could further the adaptability and accuracy of autonomous systems in rapidly changing environments.

Conclusion

The paper provides a comprehensive paper on autonomy-oriented digital twins, with significant implications for autonomous vehicle deployment. By enhancing simulation models' fidelity and allowing seamless transitions between reality and simulations, the research contributes to advancing autonomous vehicle technology. The practical implementations not only underscore the flexibility of the Autoware stack but also set a precedent for future innovations in the field of autonomous systems. The deployment of the first off-road Autoware applications further demonstrates the potential for expanding the operational domains of contemporary autonomous navigation systems.