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

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.

[1]  Amanda Nolen,et al.  Thematic analysis of qualitative research data: Is it as easy as it sounds? , 2018, Currents in pharmacy teaching & learning.

[2]  Wei Zhang,et al.  An interview study exploring Tesla drivers' behavioural adaptation. , 2018, Applied ergonomics.

[3]  Helen C. Purchase,et al.  Experimental Human-Computer Interaction - A Practical Guide with Visual Examples , 2012 .

[4]  G McGwin,et al.  Characteristics of traffic crashes among young, middle-aged, and older drivers. , 1999, Accident; analysis and prevention.

[5]  Cheryl Tatano Beck,et al.  Generalization in quantitative and qualitative research: myths and strategies. , 2010, International journal of nursing studies.

[6]  Claude Marin-Lamellet,et al.  Difficulties experienced by older drivers during their regular driving and their expectations towards Advanced Driving Aid Systems and vehicle automation , 2018 .

[7]  P. Gill,et al.  Methods of data collection in qualitative research: interviews and focus groups , 2008, BDJ.

[8]  H. Turunen,et al.  Theme development in qualitative content analysis and thematic analysis , 2016 .

[9]  Michel Bédard,et al.  Facilitators, barriers and needs in the use of adaptive driving strategies to enhance older drivers’ mobility: Importance of openness, perceptions, knowledge and support , 2016 .

[10]  Anat Gesser-Edelsburg,et al.  "Virtual" versus "actual" parental accompaniment of teen drivers: A qualitative study of teens' views of in-vehicle driver monitoring technologies , 2013 .

[11]  Liisa Hakamies-Blomqvist,et al.  OLDER DRIVERS' ABERRANT DRIVING BEHAVIOUR, IMPAIRED ACTIVITY, AND HEALTH AS REASONS FOR SELF-IMPOSED DRIVING LIMITATIONS , 2002 .

[12]  A. Pradhan,et al.  A qualitative examination of drivers’ responses to partially automated vehicles , 2018, Transportation Research Part F: Traffic Psychology and Behaviour.

[13]  V. Braun,et al.  Using thematic analysis in psychology , 2006 .

[14]  Brenda Vrkljan,et al.  Driving with advanced vehicle technology: A qualitative investigation of older drivers' perceptions and motivations for use. , 2017, Accident; analysis and prevention.

[15]  Jodi Aronson.  A Pragmatic View of Thematic Analysis , 1995 .

[16]  Jing Feng,et al.  Age differences in the takeover of vehicle control and engagement in non-driving-related activities in simulated driving with conditional automation. , 2017, Accident; analysis and prevention.

[17]  Phil Blythe,et al.  Fork in the road: in-vehicle navigation systems and older drivers , 2013 .

[18]  B. Crabtree,et al.  The qualitative research interview , 2006, Medical education.

[19]  Weiwei Guo,et al.  Optimising landmark-based route guidance for older drivers , 2016 .

[20]  Nigel Fielding,et al.  Using Computers in Qualitative Research , 1991 .

[21]  Kathrin Zeeb,et al.  Why is steering not the same as braking? The impact of non-driving related tasks on lateral and longitudinal driver interventions during conditionally automated driving , 2017 .

[22]  C. Goodman.  Quality of life from the perspectives of older people , 2004 .

[23]  Beth T. Stalvey,et al.  Driving avoidance and functional impairment in older drivers. , 1998, Accident; analysis and prevention.

[24]  Klaus Bengler,et al.  Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations , 2016, Hum. Factors.

[25]  Wendy Ju,et al.  Exploring Transitional Automation with New and Old Drivers , 2016 .

[26]  A. Bowling,et al.  Quality of life from the perspectives of older people , 2004, Ageing and Society.

[27]  L Hakamies-Blomqvist,et al.  Why do older drivers give up driving? , 1998, Accident; analysis and prevention.

[28]  M. O’Reilly,et al.  ‘Unsatisfactory Saturation’: a critical exploration of the notion of saturated sample sizes in qualitative research , 2013 .

[29]  G. Hong,et al.  Predictors of driving cessation in community-dwelling older adults: A 3-year longitudinal study , 2018 .

[30]  Phil Blythe,et al.  Investigation of older driver's takeover performance in highly automated vehicles in adverse weather conditions , 2018, IET Intelligent Transport Systems.

[31]  C. Musselwhite,et al.  Exploring older drivers’ perceptions of driving , 2010, European journal of ageing.

[32]  Carla Seddon,et al.  Lifestyles and social participation , 2011 .

[33]  Laura Johnson,et al.  How Many Interviews Are Enough? , 2006 .

[34]  Victoria Gitelman,et al.  Exploring the characteristics of potential and current users of mobility scooters, among older people in Israel , 2017 .

[35]  Neville A. Stanton,et al.  Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control , 2017, Hum. Factors.

[36]  Kerry Armstrong,et al.  Age and gender comparisons of driving while sleepy : behaviours and risk perceptions , 2011 .

[37]  D. H Metz,et al.  Mobility of older people and their quality of life , 2000 .

[38]  J. Sim,et al.  Saturation in qualitative research: exploring its conceptualization and operationalization , 2017, Quality & Quantity.

[39]  Oli Mival,et al.  Mutual inspiration in the development of new technology for older people. , 2003 .

[40]  J Fofanova,et al.  Fahrfremde Taetigkeiten aelterer Fahrer. Ergebnisse einer face-to-face-Befragung / Distraction in older drivers. A face-to-face-interview study , 2012 .

[41]  M. Falkenstein,et al.  Functional Changes and Driving Performance in Older Drivers: Assessment and Interventions , 2016, Geriatrics.

[42]  Phil Blythe,et al.  The application of in-vehicle systems for elderly drivers , 2010 .

[43]  Judith Lynne Charlton,et al.  Toward best practice in Human Machine Interface design for older drivers: A review of current design guidelines. , 2017, Accident; analysis and prevention.

[44]  Joseph F. Coughlin,et al.  In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers , 2014 .

[45]  Brian N. Fildes,et al.  Characteristics of Older Drivers who Adopt Self-Regulatory Driving Behaviours , 2006 .

[46]  Phil Blythe,et al.  Effect of intelligent speed adaptation technology on older drivers' driving performance , 2015 .

[47]  Klaus Bengler,et al.  The influence of age on the take-over of vehicle control in highly automated driving , 2016 .

[48]  Klaus Bengler,et al.  Trust in Automation – Before and After the Experience of Take-over Scenarios in a Highly Automated Vehicle☆ , 2015 .

[49]  J. Fereday,et al.  Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development , 2006 .

[50]  Mark J.M. Sullman,et al.  Driving distractions: An insight gained from roadside interviews on their prevalence and factors associated with driver distraction , 2017 .

[51]  L. Andreone,et al.  Driver-vehicle interfaces and interaction: where are they going? , 2009 .

[52]  Richard E. Boyatzis,et al.  Transforming Qualitative Information: Thematic Analysis and Code Development , 1998 .