Abstract

LIKE many complex and time-critical domains, air traffic control (ATC) is facing a fundamental modernization that builds on the use of more advanced automation (represented by SESAR in Europe and NextGen in the United States). The current function allocation-based relationship between controller and machine is envisioned to evolve to a more fluid, continuous and mutually coordinated team relationship. Consequently, the controller is expected to assume a supervisory and monitoring role, while relinquishing much of the tactical “hands-on” tasks to automation. ATC automation, in turn, is expected to grow in intelligence and its cognitive abilities to become more of a team member providing decision support and acting more autonomously. In association to these changes, one of the most pressing human factors challenges is how we can design automation that is embraced, accepted and trusted by the controller...

Abstract

LIKE many complex and time-critical domains, air traffic control (ATC) is facing a fundamental modernization that builds on the use of more advanced automation (represented by SESAR in Europe and NextGen in the United States). The current function allocation-based relationship between controller and machine is envisioned to evolve to a more fluid, continuous and mutually coordinated team relationship. Consequently, the controller is expected to assume a supervisory and monitoring role, while relinquishing much of the tactical “hands-on” tasks to automation. ATC automation, in turn, is expected to grow in intelligence and its cognitive abilities to become more of a team member providing decision support and acting more autonomously. In association to these changes, one of the most pressing human factors challenges is how we can design automation that is embraced, accepted and trusted by the controller...

Abstract

Conventional road transport has negative impact on the environment. Stimulating eco-driving through feedback to the driver about his/her energy conservation performance has the potential to reduce CO"inf"2"/inf" emissions and promote fuel cost savings. Not all drivers respond well to the same type of feedback. Research has shown that different drivers are attracted to different types of information and feedback. The goal of this paper is to explore which different driver segments with specific psychographic characteristics can be distinguished, how these characteristics can be used in the development of an ecodriving support system and whether tailoring eco-driving feedback technology to these different driver segments will lead to increased acceptance and thus effectiveness of the eco feedback technology. The driver segments are based on the value orientation theory and learning orientation theory. Different possibilities for feedback were tested in an exploratory study in a driving simulator. An explorative study was selected since the choice of the display (how and when the information is presented) may have a strong impact on the results. This makes testing of the selected driver segments very difficult. The results of the study nevertheless suggest that adapting the display to a driver segment showed an increase in acceptance in certain cases. The results showed small differences for ratings on acceptation, ease of use, favouritism and a lower general rating between matched (e.g., learning display with learning oriented drivers) and mismatched displays (e.g., learning display with performance oriented drivers). Using a display that gives historical feedback and incorporates learning elements suggested a non-verifiable increase in acceptance for learning oriented drivers. However historical feedback and learning elements may be less effective for performance oriented drivers, who may need comparative feedback and game elements to improve energy conserving driving behaviour. © 2015.