Human Driver Digital Twins: Definitions, Concepts, Use-Cases, and Engineering Challenges

Kurzfassung

While the concept of the Human Digital Twin has been proposed within the automotive domain, a structured framework to define and operationalize it is currently lacking. This paper interrogates the concept of Human Driver Digital Twins (HDDTs), systematically distinguishing them from Human Driver Digital Models (HDDMs) and Human Driver Digital Shadows (HDDSs) based on data integration levels and bidirectional feedback loops. We examine a broad spectrum of automotive use cases to assess how the HDDT paradigm can enhance existing applications and where conventional driver modeling methodologies encounter functional limitations. Furthermore, we acknowledge engineering challenges, such as data sovereignty, standardized interaction protocols, and modular internal architectures. Ultimately, this work highlights how an HDDT framework allows systems to transcend the boundaries of static driver modeling, enabling persistent, adaptive, and personalized representations of the human driver.