Evaluation of the effects of age-friendly human-machine interfaces on the driver's takeover performance in highly automated vehicles

Research output: Contribution to journalArticle

Authors

  • Shuo Li
  • Phil Blythe
  • Weihong Guo
  • Anil Namdeo
  • Simon Edwards
  • Paul Goodman
  • Graeme Hill

External departments

  • Newcastle University

Details

Original languageEnglish
Pages (from-to)78-100
Number of pages23
JournalTransportation Research Part F: Traffic Psychology and Behaviour
Volume67
Early online date31 Oct 2019
DOIs
Publication statusPublished - 1 Nov 2019
Externally publishedYes
Publication type

Research output: Contribution to journalArticle

Abstract

The ability to continue driving into old age is strongly associated with older adults’ mobility and wellbeing for those that have been dependant on car use for most of their adult lives. The emergence of highly automated vehicles (HAVs) may have the potential to allow older adults to drive longer and safer. In HAVs, when operating in automated mode, drivers can be completely disengaged from driving, but occasionally they may be required to take back the control of the vehicle. The human-machine interfaces in HAVs play an important role in the safe and comfortable usage of HAVs. To date, only limited research has explored how to design age-friendly HMIs in HAVs and evaluate their effectiveness. This study designed three HMI concepts based on older drivers’ requirements, and conducted a driving simulator investigation with 76 drivers (39 older drivers and 37 younger drivers) to evaluate the effect and relative merits of these HMIs on drivers’ takeover performance, workload and attitudes. Results showed that the ‘R + V’ HMI (informing drivers of vehicle status together with providing the reasons for the manual driving takeover request) led to better takeover performance, lower perceived workload and highly positive attitudes, and is the most beneficial and effective HMI. In addition, The ‘V’ HMI (verbally informing the drivers about vehicle status, including automation mode and speed, before the manual driving takeover request) also had a positive effect on drivers’ takeover performance, perceived workload and attitudes. However, the ‘R’ HMI (solely informing drivers about the reasons for takeover as part of the takeover request) affected older and younger drivers differently, and resulted in deteriorations in performance and more risky takeover for both older and younger drivers compared to the baseline HMI. Moreover, significant age difference was observed in the takeover performance and perceived workload. Above all, this research highlights the significance of taking account older drivers’ requirements into the design of HAVs and the importance of collaboration between automated vehicle and cooperative ITS research communities.

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