Future crewed missions to the Moon will face significant environmental and operational challenges, posing risks to the safety and performance of astronauts navigating its inhospitable surface. Whilst head-up displays (HUDs) have proven effective in providing intuitive navigational support on Earth, the design of novel human-spaceflight solutions typically relies on costly and time-consuming analogue deployments, leaving the potential use of lunar HUDs largely under-explored. This paper explores an alternative approach by simulating navigational HUD concepts in a high-fidelity Virtual Reality (VR) representation of the lunar environment. In evaluating these concepts with astronauts and other aerospace experts (n=25), our mixed methods study demonstrates the efficacy of simulated analogues in facilitating rapid design assessments of early-stage HUD solutions. We illustrate this by elaborating key design challenges and guidelines for future lunar HUDs. In reflecting on the limitations of our approach, we propose directions for future design exploration of human-machine interfaces for the Moon.

Future automated driving systems (ADS) are discussed as having the ability to “override” driver control inputs. Yet, little is known about how drivers respond to this, nor how a human-machine interaction (HMI) for them should be designed. This work identifies intervention types associated with an ADS that has change control authority and outlines an experiment method which simulates a deficit in driver situation awareness, enabling the study of their responses to interventions in a controlled environment. In a simulator study (N = 18), it was found that drivers express more negative valence when their control input is blocked (p = .046) than when it is taken away. In safety-critical scenarios, drivers respond more positively to interventions (p = .021) and are willing to give the automation more control (p = .018). An experimental method and HMI design insights are presented and ethical questions about the development of automated driving are provoked.

With increasing autonomous capabilities and intelligence of machines and interconnectivity of humans and machines, interaction and cooperation become even more crucial for socio-cyber-physical systems. How can we organize the exploding number of design options in a way that it is easy for humans to understand and control these systems? Patterns for the design of interaction and cooperation could be the key for describing problems and generalizable solution patterns, which can be instantiated again in individual solutions. Modeling, matching and instantiating could be the fundamental use cases for those patterns. Understanding, designing, engineering and using are the essential activities in an interplay of researchers, designers, engineers and users of human-machine systems. A realistic dream is to have global databases of human-machine patterns and their effects so that the wheel does not have to be re-invented several times and common knowledge about patterns can be shared in our community.