Learning from Humans – Building for Humans

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Cyber-physical systems are increasing in their technical potentials and their applicability for different usage scenarios. Still the interaction concept between human and technical system is a crucial success factor for safe, efficient and acceptable usage of these systems. To enable cooperation in this constellation this project will investigate the interrelation between human perceptive capabilities, mental workload and human trust into the CPS. Especially Human Cyber-Physical Systems (HCPS) for traffic applications are of high societial relevance and dynamic in their development. Following a basic cooperation principle an HCPS should use the strength of both human and machine, and compensates their individual limitations. Users with visual limitations put high expectations into the capabilities of automated driving systems. Automated driving systems still try to mimic human strategies in complex traffic situations.Learning from Humans in this project means that experiments with participants successfully driving under visual impairments should provide knowledge how to compensate incomplete technical sensing. Additionally we will learn how cognitive load is related to human decision making and how increasing load is compensated by drivers. The third aspect focuses how these factors influence trust into an automated driving assistance system. Building for Humans means that based on this knowledge a suitable HMI concept shall be implemented that provides information from the technical system towards the user decreasing workload, increasing trust and leading to efficient decision behaviour. The goal is an HCPS adapted to human requirements regarding perception, workload and trust. For this reason the research partners in this project will conduct highly synchronized driving simulation experiments and share their specific measurement expertise. This includes simulation practice, eye-tracking, fMRI measurement, automated driving implementation and interaction prototyping. The driving scenarios of different complexity will be defined in a joint effort, implemented by one partner and distributed to the other partners. This work plan leads to an interdisciplinary cooperation between human factors, cognitive science, neuroscience, and computer science.The data gathered in these distributed experiments will be used to specify modules dedicated to perception, cognitive load and trust. These modules will be integrated into a joint model using Dynamic Bayesian Networks describing the interrelations and their influences on decision processes.