The growing and risky industry of nomadic apps for drivers
| cic.institucionOrigen | Laboratorio de Investigación y Formación en Informática Avanzada (LIFIA) | es |
| cic.isFulltext | true | es |
| cic.isPeerReviewed | true | es |
| cic.lugarDesarrollo | Laboratorio de Investigación y Formación en Informática Avanzada | es |
| cic.version | info:eu-repo/semantics/publishedVersion | es |
| dc.date.accessioned | 2021-03-30T18:25:46Z | |
| dc.date.available | 2021-03-30T18:25:46Z | |
| dc.identifier.uri | https://digital.cic.gba.gob.ar/handle/11746/10861 | |
| dc.title | The growing and risky industry of nomadic apps for drivers | en |
| dc.type | Documento de conferencia | es |
| dcterms.abstract | HCI researchers have worked for decades defining methods and techniques to assess the attention demands of in-vehicle information systems (IVIS). Acceptance test methods have been proposed that must be passed for the safe use of IVIS. Most of these methods require expensive test environments and highly trained personnel for its implementation. This article makes a review of those strategies with focus in the cost and development process phase. In the realm of mobile application ecosystems (aka apps ), guidelines and certification programs exist. Apps must pass them to be considered as automotiveready systems or to integrate with OEM infotainment devices. However, getting into the category of certified applications does not guarantee full compliance with the criteria established by formal methods accepted by the automotive industry and international standards. Moreover, many studies show the high risk of using IVIS while driving, which lead to consider that the current predominant approaches to assess attention demands of automotive apps and to guide IVIS design are not enough. Efficient cost-benefit methods applicable in early phases of application development, as well as context-adaptive interfaces have the potential to contribute to the improvement of safe driving environments. | en |
| dcterms.creator.author | Carvajal, Carlos | es |
| dcterms.creator.author | Rodríguez, Andrés | es |
| dcterms.creator.author | Fernández, Alejandro | es |
| dcterms.identifier.url | Recurso completo | es |
| dcterms.isPartOf.issue | HCI 2020 | es |
| dcterms.isPartOf.series | VI Iberoamerican Conference of Computer Human Interaction | es |
| dcterms.issued | 2020-09 | |
| dcterms.language | Inglés | es |
| dcterms.license | Attribution 4.0 International (BY 4.0) | es |
| dcterms.subject | IVIS | en |
| dcterms.subject | driver attention | en |
| dcterms.subject | empirical methods | en |
| dcterms.subject | visual demand | en |
| dcterms.subject | cognitive demand | en |
| dcterms.subject | analytical techniques | en |
| dcterms.subject.materia | Ciencias de la Computación | es |
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