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Can You Still Look Up? Remote Rotary Controller vs. Touchscreen
Technical Paper
2017-01-1386
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
The popularity of new Human-Machine-Interfaces (HMIs) comes with growing concerns for driver distraction. In part, this concern stems from a rising challenge to design systems that can make functions accessible to drivers while maintaining drivers’ ability to cope with the complex driving task. Therefore, engineers need assessment methods which can evaluate how well a user interface achieves the dual-goal of making secondary tasks accessible, while allowing safe driving. Most prior methods have emphasized measuring off-road glances during HMI use. An alternative to this is to consider both on-road and off-road glances, as done in Kircher and Ahlstrom’s AttenD algorithm [1]. In this study, we compared two types of prevalent visual-manual user interfaces based on AttenD. The two HMIs of interest were a touchscreen-based interface (already in production) and a remote-rotary-controller-based interface (a high-fidelity prototype). Five in-vehicle tasks were evaluated, including a continuous-control task, a shortcut task, a menu-navigation task, a list-operation task and a function-switch task. Sixteen participants’ glance behavior was manually coded to apply AttenD. Results suggested that with a higher-positioned display and haptic feedback, the rotary-controller helped drivers maintain attention to the roadway better than the touchscreen-based interface for simple continuous control and shortcut tasks. For the more complex tasks, the results were mixed with interesting insights. Additionally, the AttenD also revealed significant individual differences in attention management strategy. In summary, AttenD-like algorithms not only can compare different HMIs, but also can reveal individual attention allocation strategies.
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Zhang, Y., Angell, L., Pala, S., Hara, T. et al., "Can You Still Look Up? Remote Rotary Controller vs. Touchscreen," SAE Technical Paper 2017-01-1386, 2017, https://doi.org/10.4271/2017-01-1386.Data Sets - Support Documents
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References
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