Investigation of older drivers’ requirements of the human-machine interaction in highly automated vehicles

Li, Shuo, Blythe, Phil, Guo, Weihong and Namdeo, Anil (2019) Investigation of older drivers’ requirements of the human-machine interaction in highly automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 62. pp. 546-563. ISSN 1369-8478

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Official URL: https://doi.org/10.1016/j.trf.2019.02.009

Abstract

The population of older drivers is increasing in size. However, age-related functional decline potentially reduce their safe driving ability and thereby their wellbeing may decline. Fortunately, the forthcoming highly automated vehicles (HAVs) may have the potential to enhance the mobility of older drivers. HAVs would introduce a revolutionary human-machine interaction in which drivers can be completely disengaged from driving, and their control would be required occasionally. In order to inform the design of an age-friendly human-machine interaction in HAVs, several semi-structured interviews were conducted with 24 older drivers (mean = 71.50 years, SD = 5.93 years; 12 female, 12 male) to explore their opinions of and requirements towards HAV after they had hands-on experience with a HAV on a driving simulator. Results showed that older drivers were positive towards HAVs and welcomed the hands-on experience with HAVs. In addition, they wanted to retain physical and potential control over the HAVs, and would like to perform a range of non-driving related tasks in HAVs. Meanwhile, they required an information system and a monitoring system to support their interactions with HAVs. Moreover, they required the takeover request of HAVs to be adjustable, explanatory and hierarchical, and they would like the driving styles of HAVs to be imitative and corrective. Above all, this research provides recommendations to inform the design of age-friendly human-machine interactions in HAVs and highlights the importance of considering the older drivers’ requirements when designing and developing automated vehicles.

Item Type: Article
Uncontrolled Keywords: Highly automated vehicles, Human factors, Human-machine interaction, Older drivers, Semi-structured interview, Takeover control, User requirements
Subjects: F800 Physical and Terrestrial Geographical and Environmental Sciences
K400 Planning (Urban, Rural and Regional)
L700 Human and Social Geography
Department: Faculties > Engineering and Environment > Geography and Environmental Sciences
Depositing User: Rachel Branson
Date Deposited: 22 Jun 2020 11:02
Last Modified: 22 Jun 2020 11:15
URI: http://nrl.northumbria.ac.uk/id/eprint/43522

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