Camilla Grane

Menu Selection with a Rotary Device Founded on Haptic and/or Graphic Information

The purpose of the study reported in this paper was to compare three different types of interfaces providing: haptic information, graphic information, or combined haptic and graphic information in an easy menu selection task. The results suggested that the combined haptic and graphic interface was preferable to the graphic interface regarding accuracy, while both interfaces were fundamentally equal concerning completion time. The results also indicated that the combined haptic and graphic interface was less mentally demanding than either the graphic or the haptic interface. Solely haptic feedback obtained, without exception, worse results than the other cases.

Haptic addition to a visual menu selection interface controlled by an in-vehicle rotary device

Today, several vehicles are equipped with a visual display combined with a haptic rotary device for handling in-vehicle information system tasks while driving. This experimental study investigates whether a haptic addition to a visual interface interferes with or supports secondary task performance and whether haptic information could be used without taking eyes off road. Four interfaces were compared during simulated driving: visual only, partly corresponding visual-haptic, fully corresponding visual-haptic, and haptic only. Secondary task performance and subjective mental workload were measured. Additionally, the participants were interviewed. It was found that some haptic support improved performance. However, when more haptic information was used, the results diverged in terms of task completion time and interface comprehension. Some participants did not sense all haptics provided, some did not comprehend the correspondence between the haptic and visual interfaces, and some did. Interestingly, the participants managed to complete the tasks when using haptic-only information.

Assessment selection in human-automation interaction studies: The Failure-GAM2E and review of assessment methods for highly automated driving

Highly automated driving will change drivers behavioural patterns. Traditional methods used for assessing manual driving will only be applicable for the parts of human-automation interaction where the driver intervenes such as in hand-over and take-over situations. Therefore, driver behaviour assessment will need to adapt to the new driving scenarios. This paper aims at simplifying the process of selecting appropriate assessment methods. Thirty-five papers were reviewed to examine potential and relevant methods. The review showed that many studies still relies on traditional driving assessment methods. A new method, the Failure-GAM2E model, with purpose to aid assessment selection when planning a study, is proposed and exemplified in the paper. Failure-GAM2E includes a systematic step-by-step procedure defining the situation, failures (Failure), goals (G), actions (A), subjective methods (M), objective methods (M) and equipment (E). The use of Failure-GAM2E in a study example resulted in a well-reasoned assessment plan, a new way of measuring trust through feet movements and a proposed Optimal Risk Management Model. Failure-GAM2E and the Optimal Risk Management Model are believed to support the planning process for research studies in the field of human-automation interaction.