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

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.

I. Introduction

Digital twinning of vehicles is an iconic application of digital twins, as the idea of twinning dates back to NASA's Apollo program, where at least two identical space vehicles were built to mirror the conditions of the vehicle in space and to provide decision-support during the mission based on simulations using the vehicle's twin on Earth [1].