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

Verification and validation (V&V) of autonomous vehicles (AVs) is critical to ensure operational safety, reliability, and regulatory compliance. This 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 inherent safety issues, 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, given the sheer amount of test cases that need to be executed. In such a milieu, this work proposes a framework that flexibly scales digital twin simulations within high-performance computing clusters (HPCCs) and automates the V&V process. Here, digital twins enable the creation of high-fidelity virtual representations of the AV and its operating environments, allowing extensive scenario-based testing in precisely controlled yet realistic simulations. Meanwhile, cloud-based 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 on variability analysis of a candidate autonomy algorithm to identify 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 by significantly compressing (∼ 30×) the timeline.