The technical potentials and their applicability of cyber-physical systems (CPS) are constantly increasing for different application scenarios. Nevertheless, the interaction between user and technical system is a decisive success-factor for a safe, efficient, and acceptable use of these systems. To enable cooperation in this constellation, this project will investigate the relationship between human perceptual abilities, mental workload and human trust in a CPS.
Especially Human Cyber-Physical Systems (HCPS) for transport applications are of high social relevance and dynamic in their development. According to the fundamental principle of cooperation, an HCPS should make use of the strengths of man and machine and compensate for their individual limitations. Users with visual impairments have high expectations of the possibilities offered by automated driving systems. Automated driving systems still try to imitate human strategies in complex traffic situations. In this project, learning from people means that experiments with participants who successfully drive a car with visual impairments should provide knowledge how incomplete technical perception can be compensated.
In addition, we investigate how cognitive stress is related to human decision making and how increasing cognitive stress is compensated by drivers. The third aspect is how these factors influence trust in an automated driver assistance system. Designing for humans means that based on this knowledge, a suitable HMI concept should be implemented, which delivers information from the technical system to the user, which should reduce workload, increase confidence and lead to efficient decision-making behaviour. The goal is an HCPS that is adapted to the human requirements in terms of perception, workload, and trust. For this reason, the research partners in this project will conduct highly tuned driving simulation experiments and exchange their specific competences. This includes simulation practice, eye tracking, fMRI measurements, implementation of automated driving functions and interaction prototyping. Driving scenarios of varying complexity are defined jointly, 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 collected in these distributed experiments will be used to specify modules dedicated to perception, cognitive load and trust. These modules will be integrated into a common model that describes the relationships and their influences on decision-making processes.
Trende Alexander, Hartwich Franziska, Schmidt Cornelia, Fränzle Martin; International Conference on Human-Computer Interaction; 0Juli / 2020