Action-integrated modeling and optimization of energy-related co-regulation in human-machine systems in the context of ecodriving (AMORi-2)

• Applicants: Professor Dr. Thomas Franke; Professor Dr.-Ing. Daniel Görges
• Subject Area: Human Factors, Ergonomics, Human-Machine Systems
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 498999989

In view of climate change and resource scarcity, the optimized integration of humans and machines to improve energy efficiency in human-technology systems is of vital importance. However, basic research in this field is still in its infancy. Specifically, there is a lack of interdisciplinary foundational knowledge on human energy-related action regulation and the integrated regulation of energy efficiency by humans and machines, theoretical framework models, and systematized research methods. In the first project (AMORi-1), a conceptual framework model for energy-related human-machine integration was developed and EcoSimLab, a holistic simulation environment for research in human-technology interaction in the field of energy-optimized driving (EcoDriving) as a promising research context, was created. The central research objective of the planned follow-up project (AMORi-2) is to systemize the co-regulation of human and machine agents along the devised model components in energy-relevant interaction cases in EcoDriving in a theory-guided manner and, based on this, to establish a sound theoretical and methodological foundation in addition to empirical contributions. In the underlying framework model, the co-regulation of human and machine agents is described from a control-theoretic perspective through a meshed control structure. Key model components within the control structure are the input function (technical sensor systems and human reception), the reference function (technical target variables and human objectives) and the output function (technical control and human actions). The guiding hypothesis of the follow-up project is that an adaptive co-regulation between humans and machines through effective integration mechanisms at the input, reference, and output function level leads to an optimized energy efficiency and an improved interaction experience compared to purely optimization-based energy displays, leading to the following central research questions: To what extent do integration mechanisms in the (F1) input function (co-representation & adaptation of situation models), (F2) reference function (coordination & alignment of the target function), and (F3) output function (co-representation & adaptation of the option space) lead to an improved co-regulation? (F4) To what extent are the integration mechanisms moderated by variables of user diversity? (F5) To what extent can data-efficient driving behavior models to optimize the human-machine integration in the context of EcoDriving be realized that are transferable, adaptive, and comprehensible to drivers in terms of their dynamics? In line with these questions, the model components will be developed and operationalized in the work programme based on concepts from control theory and engineering psychology and evaluated in driving simulator studies.

• DFG Programme: Research Grants