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

TL;DR

This paper introduces a scalable, modular virtual proving ground using digital twins and high-performance computing to accelerate the verification and validation of autonomous vehicles, significantly reducing testing time and costs.

Contribution

It presents a novel framework combining digital twins with HPC for scalable, interoperable, and efficient AV V&V, demonstrated through a large-scale case study.

Findings

Accelerates V&V process by approximately 30 times

Enables large-scale scenario testing across multiple HPC architectures

Demonstrates high fidelity and interoperability of digital twin-based testing

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.