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

Lookup NU author(s): Dr Shuo Li, Professor Phil Blythe, Dr Anil Namdeo, Simon Edwards, Dr Paul Goodman, Dr Graeme Hill

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


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.


Publication metadata

Author(s): Li S, Blythe P, Guo W, Namdeo A, Edwards S, Goodman P, Hill H

Publication type: Article

Publication status: Published

Journal: Transportation Research Part F: Traffic Psychology and Behaviour

Year: 2019

Volume: 67

Pages: 78-100

Print publication date: 01/11/2019

Online publication date: 31/10/2019

Acceptance date: 20/10/2019

Date deposited: 01/11/2019

ISSN (print): 1369-8478

ISSN (electronic): 1873-5517

Publisher: Elsevier Ltd.

URL: https://doi.org/10.1016/j.trf.2019.10.009

DOI: 10.1016/j.trf.2019.10.009

Data Source Location: https://doi.org/10.25405/data.ncl.9693533.v1

Notes: https://www.sciencedirect.com/science/article/pii/S1369847819304176


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