Maturity of Vehicle Digital Twins: From Monitoring to Enabling Autonomous Driving

TL;DR

The paper addresses how mature vehicle digital twins need to be to support monitoring, efficiency, and autonomous driving across freight, passenger, and autonomous domains. It synthesizes recent DT developments and applies a six-level maturity framework to classify DT capabilities, distinguishing DTPs, DTIs, and DTAs as vehicles evolve from design validation to cross-vehicle collaboration. The findings show that most DTs operate at level-3 for real-time monitoring, with level-5 deployments in closed environments enabling restricted autonomy, while level-6 interoperability is required for full fleet-wide autonomous decision-making. The work highlights the practical implications for industry roadmaps, signaling that DTs are transitioning from design-time tools to operational assets that can enable platooning, fleet management, and urban mobility optimization, albeit with remaining challenges in standardization, interoperability, safety, and security.

Abstract

Digital twinning of vehicles is an iconic application of digital twins, as the concept of twinning dates back to the twinning of NASA space vehicles. Although digital twins (DTs) in the automotive industry have been recognized for their ability to improve efficiency in design and manufacturing, their potential to enhance land vehicle operation has yet to be fully explored. Most existing DT research on vehicle operations, aside from the existing body of work on autonomous guided vehicles (AGVs), focuses on electrified passenger cars. However, the use and value of twinning varies depending on the goal, whether it is to provide cost-efficient and sustainable freight transport without disruptions, sustainable public transport focused on passenger well-being, or fully autonomous vehicle operation. In this context, DTs are used for a range of applications, from real-time battery health monitoring to enabling fully autonomous vehicle operations. This leads to varying requirements, complexities, and maturities of the implemented DT solutions. This paper analyzes recent trends in DT-driven efficiency gains for freight, public, and autonomous vehicles and discusses their required level of maturity based on a maturity tool. The application of our DT maturity tool reveals that most DTs have reached level 3 and enable real-time monitoring. Additionally, DTs of level 5 already exist in closed environments, allowing for restricted autonomous operation.

Figures (4)

• Figure 1: Applications of DTs throughout their lifecycle. Adapted from liu2021review.
• Figure 2: Application of the maturity levels in the DT/reality context.
• Figure 3: Maturity levels and their main enabling potential in the vehicle context. Adapted from klar2023digital.
• Figure 4: Application of digital twins for different types of vehicles.