An Open Road Evaluation of a Self-Driving Vehicle Human-Machine Interface Designed for Visually Impaired Users

Fully autonomous or "self-driving" vehicles are an emerging technology that may hold tremendous mobility potential for individuals who are visually impaired who have been previously disadvantaged by an inability to operate conventional motor vehicles. Prior studies however, have suggested...

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Veröffentlicht in:International journal of human-computer interaction 2019-07, Vol.35 (11), p.1018-1032
Hauptverfasser: Brinkley, Julian, Posadas, Brianna, Sherman, Imani, Daily, Shaundra B., Gilbert, Juan E.
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Sprache:eng
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Zusammenfassung:Fully autonomous or "self-driving" vehicles are an emerging technology that may hold tremendous mobility potential for individuals who are visually impaired who have been previously disadvantaged by an inability to operate conventional motor vehicles. Prior studies however, have suggested that these consumers have significant concerns regarding the accessibility of this technology and their ability to effectively interact with it. We present the results of a quasi-naturalistic study, conducted on public roads with 20 visually impaired users, designed to test a self-driving vehicle human-machine interface. This prototype system, ATLAS, was designed in participatory workshops in collaboration with visually impaired persons with the intent of satisfying the experiential needs of blind and low vision users. Our results show that following interaction with the prototype, participants expressed an increased trust in self-driving vehicle technology, an increased belief in its likely usability, an increased desire to purchase it and a reduced fear of operational failures. These findings suggest that interaction with even a simulated self-driving vehicle may be sufficient to ameliorate feelings of distrust regarding the technology and that existing technologies, properly combined, are promising solutions in addressing the experiential needs of visually impaired persons in similar contexts.
ISSN:1044-7318
1532-7590
1044-7318
DOI:10.1080/10447318.2018.1561787