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

Lookup NU author(s): Dr Shuo Li, Professor Phil Blythe, Dr Anil Namdeo

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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.


Publication metadata

Author(s): Li S, Blythe P, Guo W, Namdeo A

Publication type: Article

Publication status: Published

Journal: Transportation Research Part F: Traffic Psychology and Behaviour

Year: 2019

Volume: 62

Pages: 546-563

Print publication date: 01/04/2019

Online publication date: 28/02/2019

Acceptance date: 14/02/2019

Date deposited: 01/03/2019

ISSN (print): 1369-8478

ISSN (electronic): 1873-5517

Publisher: Elsevier

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

DOI: 10.1016/j.trf.2019.02.009


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