Digital Twins in the Cloud: A Modular, Scalable and Interoperable Framework for Accelerating Verification and Validation of Autonomous Driving Solutions

2025-05-19

AI Summary

To address the limitations of real-world testing and the trade-off between fidelity and scalability in simulation-based verification and validation (V&V) of autonomous vehicles (AVs), this work proposes a cloud-native digital twin–enabled virtual testbed. We introduce a novel, modular, cross-platform interoperable digital twin framework that integrates Kubernetes-based container orchestration, high-performance parallel simulation, distributed data processing, and three-layer (perception, planning, control) mutation testing. The framework enables dynamic resource scheduling across heterogeneous high-performance computing clusters and supports end-to-end automated V&V. Evaluated on 256 test cases, it achieves approximately 30× reduction in V&V cycle time. The framework has been successfully deployed and stabilized on two distinct high-performance computing cluster (HPCC) architectures, enabling continuous, large-scale, high-fidelity AV simulation and validation in shared computing environments.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Verification and validation (V&V) of autonomous vehicles (AVs) typically requires exhaustive testing across a variety of operating environments and driving scenarios including rare, extreme, or hazardous situations that might be difficult or impossible to capture in reality. Additionally, physical V&V methods such as track-based evaluations or public-road testing are often constrained by time, cost, and safety, which motivates the need for virtual proving grounds. However, the fidelity and scalability of simulation-based V&V methods can quickly turn into a bottleneck. In such a milieu, this work proposes a virtual proving ground that flexibly scales digital twins within high-performance computing clusters (HPCCs) and automates the V&V process. Here, digital twins enable high-fidelity virtual representation of the AV and its operating environments, allowing extensive scenario-based testing. Meanwhile, HPCC infrastructure brings substantial advantages in terms of computational power and scalability, enabling rapid iterations of simulations, processing and storage of massive amounts of data, and deployment of large-scale test campaigns, thereby reducing the time and cost associated with the V&V process. We demonstrate the efficacy of this approach through a case study that focuses on the variability analysis of a candidate autonomy algorithm to identify potential vulnerabilities in its perception, planning, and control sub-systems. The modularity, scalability, and interoperability of the proposed framework are demonstrated by deploying a test campaign comprising 256 test cases on two different HPCC architectures to ensure continuous operation in a publicly shared resource setting. The findings highlight the ability of the proposed framework to accelerate and streamline the V&V process, thereby significantly compressing (~30x) the timeline.

Problem

• Enables scalable digital twins for autonomous vehicle testing
• Accelerates verification and validation using high-performance computing
• Reduces time and cost of autonomous system evaluations

Innovation

• Modular digital twins for AV testing
• Scalable HPCC-based simulation framework
• Automated V&V process acceleration