AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education (2212.05241v5)

Abstract: Prototyping and validating hardware-software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt towards developing such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.

• The paper introduces a comprehensive digital twin ecosystem that integrates Testbed, Simulator, and Devkit for robust prototyping of autonomous driving solutions.
• The paper demonstrates a modular design that supports diverse use cases like autonomous parking and intersection traversal using advanced simulation and deep learning.
• The paper highlights the platform's potential to democratize research by reducing prototyping costs and bridging simulation-to-reality gaps with open-source tools.

AutoDRIVE: An Integrated Digital Twin Ecosystem for Autonomous Driving

AutoDRIVE represents a significant advance in supporting autonomous driving research through a holistic digital twin ecosystem that converges software and hardware testing components. It is designed to offer a comprehensive, flexible, and integrated solution for both prototyping and deploying autonomous driving solutions. In this paper, the authors describe the ecosystem's key components, its modular design, and its ability to synergize interaction within intelligent transportation systems.

Overview and Features

AutoDRIVE features three major platforms: the Testbed, Simulator, and Devkit. This triptych of tools allows for robust prototyping and validation. The Testbed consists of a scaled vehicle named Nigel, which includes a realistic sensor suite and actuation system, closely mimicking a full-scale vehicle in terms of kinodynamic constraints. The supporting modular infrastructure features components such as terrain modules and traffic elements, supporting both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications.

The AutoDRIVE Simulator acts as a digital counterpart to the Testbed and provides a high-fidelity virtual environment for simulating both vehicle and infrastructure dynamics. Leveraging advanced physics engines and 3D visualization capabilities, the simulator allows for the accelerated development of autonomous solutions, reduces prototyping costs, and helps transition from simulation to real-world deployment (sim2real).

Use Cases Highlighting Advanced Features

The paper elaborates on four core use cases that illustrate AutoDRIVE’s capabilities in supporting cutting-edge autonomous driving applications:

1. Autonomous Parking: The ecosystem facilitates the development of modular autonomy stacks. Utilizing probabilistic robotics for mapping and path planning, the Testbed enhances precision and reliability within controlled environments.
2. Behavioral Cloning: The paper highlights how AutoDRIVE can serve as a platform for deploying and validating deep imitation learning models, facilitating sim2real transition and bolstering end-to-end autonomy stacks.
3. Intersection Traversal: With deep reinforcement learning and V2V communication features, AutoDRIVE can support multi-agent coordination strategies, simulating complex social interactions among autonomous vehicles in congested urban settings.
4. Smart City Management: Finally, the ecosystem's V2I capabilities enable integration into broader smart city contexts, demonstrating its utility in centralizing control for traffic management via an Internet of Things (IoT) infrastructure.

Implications and Future Directions

The AutoDRIVE Ecosystem stands out for its ability to incorporate comprehensive sensory modalities, advanced computational power, and a flexible modular design, all of which facilitate education and rapid experiments within autonomous driving domains. The paper asserts the need for ongoing improvements, suggesting broadening the ecosystem to include heterogeneous vehicles and integrate advanced simulation capabilities such as mixed reality.

By offering open-source hardware and software platforms, AutoDRIVE aims to eliminate barriers to entry in the research community and push forward the rapid prototyping of autonomous technologies. This democratization of research tools can significantly reduce cost and risk factors, making it an invaluable asset for universities and researchers with budget constraints.

In conclusion, AutoDRIVE serves as a key contribution to the growing field of autonomous driving by creating an ecosystem that balances real-world application with technological innovation. The paper presents the ecosystem as an essential tool for enabling future research and development in intelligent transportation systems, proposing an outline for continual enhancement and community-driven expansion.