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ORIGINAL RESEARCH
published: 12 June 2020
doi: 10.3389/frsc.2020.00027
Frontiers in Sustainable Cities | www.frontiersin.org 1June 2020 | Volume 2 | Article 27
Edited by:
Krzysztof Goniewicz,
Military University of Aviation, Poland
Reviewed by:
Jennifer Campos,
University Health Network, Canada
Emily Schryer,
University of Waterloo, Canada
Dea Van Lierop,
Utrecht University, Netherlands
*Correspondence:
Justin Mason
justinmason@phhp.ufl.edu
Specialty section:
This article was submitted to
Governance and Cities,
a section of the journal
Frontiers in Sustainable Cities
Received: 18 March 2020
Accepted: 14 May 2020
Published: 12 June 2020
Citation:
Classen S, Mason J, Wersal J,
Sisiopiku V and Rogers J (2020) Older
Drivers’ Experience With Automated
Vehicle Technology: Interim Analysis of
a Demonstration Study.
Front. Sustain. Cities 2:27.
doi: 10.3389/frsc.2020.00027
Older Drivers’ Experience With
Automated Vehicle Technology:
Interim Analysis of a Demonstration
Study
Sherrilene Classen 1, Justin Mason 1
*, James Wersal 1, Virginia Sisiopiku 2and
Jason Rogers 1
1Department of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL,
United States, 2Department of Civil, Construction, and Environmental Engineering, School of Engineering, University of
Alabama at Birmingham, Birmingham, AL, United States
Older adults (≥65 years) account for 20% of the US population but are over-represented
in multiple-vehicle crashes. Automated vehicles (AVs) may hold safety benefits for
older drivers, if they adopt this emerging technology. Therefore, this study is using a
randomized, crossover design with pre- and post-exposure surveys, to quantify older
drivers’ perceptions, who were exposed to a simulator running in automated mode and
riding in a highly automated shuttle (SAE Level 4). An interim analysis (N=69) compares
older drivers’ perceptions before and after exposure to the automated simulator and
automated shuttle. Early findings indicate that exposure to AV technology may positively
affect older adults’ perceptions to this emerging technology. In this study, older drivers’
trust and perceived safety increased after being exposed to the driving simulator
or automated shuttle compared to baseline. Older drivers’ perceptions of perceived
usefulness and cost of AVs, increased after being exposed to both modes of vehicle
automation compared to baseline whereas their perceptions did not change after their
first AV exposure (regardless of it was the simulator or shuttle). Exposing older adults
to an automated simulator or on-road automated shuttle may promote older adults’
acceptance and adoption of AVs.
Keywords: automated shuttle, automated vehicles, driving simulation, survey, randomized experimental design,
older drivers
INTRODUCTION
The number of older adults (≥65 years) is nearing 20% of the US population, and Florida is
leading the nation with 25% of its population being older adults (US Census Bureau, 2020). In
2017, there were almost 44 million licensed drivers aged 65 and older in the United States, a 63%
increase from 1999 (Federal Highway Administration, 2018). Driving is an important mode of
transportation for older adults that ensures mobility and independence and yields many health,
community and societal benefits (Dickerson et al., 2014, 2017a,b). But older drivers are at an
increased risk for multiple-vehicle crashes and deleterious crash-related effects (Karthaus and
Falkenstein, 2016). According to American Automobile Association (AAA), since older drivers
are more fragile, their fatality rates are 17 times higher than those between the ages of 25–64
Classen et al. Older Drivers’ Experience Automated Vehicles
years old (American Automobility Association Senior Driving,
2014). As the number of adults over 65 years of age increases
in North America, strategies and countermeasures emerge
as critical factors in preventing crashes involving drivers.
Mitigation strategies, i.e., older driver screening, assessment,
and intervention (Classen et al., 2012, 2014); enhanced vehicles
with improved safety features (Charlton et al., 2002; Koppel
et al., 2013; Bengler et al., 2014; Centers for Disease Control
Prevention, 2015); enhanced infrastructure, such as protected
left hand turn lanes or extended receiving lanes (Shechtman
et al., 2007; Classen et al., 2009); and stringent policies, such
as visual testing after age 89 before license re-issuing or in-
person (vs. mail) renewal (Levy, 1995; Morrisey and Grabowski,
2005; Classen and Awadzi, 2008; Staplin and Freund, 2013),
afford older drivers the opportunity to stay on the road—
longer and safer, while they receive the health-related benefits of
being actively engaged in their communities and participating in
societal events. One emerging alternative transportation strategy
for older drivers who are reducing driving or can no longer drive,
is the use of automated modes of transportation. Such modes
may include automated ride sharing, ride hailing, or on-demand
services (i.e., paratransit), that could support the mobility and
independence of older adults while reducing their crash risk on
the road (Robertson et al., 2019).
The deployment of automated vehicles (AVs) is viewed by
many as an emerging option that holds potential health and safety
benefits for older drivers. Older drivers may benefit from the
use of four distinct AV scenarios: automated public transport
with fixed routes and schedules; automated on-demand public
transport (i.e., automated shuttle); fleet-based shared AVs; and
privately owned AVs (Faber and van Lierop, 2020). Both urban
and peripheral areas may benefit from the availability of AVs due
to increasing needs of accessibility (Faber and van Lierop, 2020).
Older adults have a strong interest in using AVs in their daily life
to overcome current mobility and accessibility barriers via on-
demand booking and using feeder AVs for access and egress to
other modes of transport (Faber and van Lierop, 2020). However,
for such benefits to materialize, elderly transportation users
would need to accept, trust, and adopt AV technology. Recent
studies (Abraham et al., 2016, 2017; Hulse et al., 2018; Rovira
et al., 2019) assessed older adults’ perceptions of AV technology
but were limited to soliciting input via surveys. Direct interactive
experience in AVs in combination with surveys are a valuable
alternative as they may more accurately reveal the perceptions of
older drivers before and after “driving” the automated simulator
or riding in the automated shuttle (Penmetsa et al., 2019).
Simulators are already, quite ubiquitously, used to assess driving
performance, or to provide interventions, in a much safer
(than on-road) yet realistic environment (Campos et al., 2017).
Driving simulators are also frequently used to expose research
participants to vehicle automation (Kauffmann et al., 2018).
However, it is unclear whether an automated simulator will
influence drivers’ perceptions of AVs and thus needs to be
explored as this technology may be used to train users on how
to use this technology and to potentially promote technology
acceptance. Vehicle capabilities may be better understood if users
are exposed to this technology via a driving simulator or on-road
use rather than conventional alternatives (i.e., demonstration
videos or a user manual).
Highly automated vehicles are now becoming a reality
and are expected to have enormous safety, societal, and
environmental benefits. Particularly, vehicle automation has
the potential to prevent older driver crashes occurring due
to age-related declines in function resulting in human error,
enhance lifelong mobility, while also reducing pollution and
non-recurrent congestion impacts because of crash reduction
(National Highway Traffic Safety Administration, 2013, 2017).
However, highly automated vehicles can be less safe than human
drivers under certain circumstances (i.e., inclement weather).
The Society of Automotive Engineers (Society of Automotive
Engineers International, 2016) defined six levels of AVs, ranging
from no automation (Level 0) to full automation (Level 5). The
focus of this paper will be on highly AVs at the SAE Level 4,
because that is the classification of the EasyMile EZ10, automated
shuttle used in this study. In this study, older drivers were
exposed to the automated shuttle and to the driving simulator
which is a representation of a privately-owned or shared-use AV.
Recent studies have suggested that AVs should be safer
than human drivers if individuals are to adopt and accept this
technology (Waycaster et al., 2018; Shladover and Nowakowski,
2019). However, in the context of highly automated vehicles,
which is the focus of this study, full acceptance and trust
may lead to complacency and misuse of the system. As such,
trust must be calibrated with the capabilities of the system
to prevent distrust, overtrust, or overreliance (Kraus et al.,
2019). The public will be less likely to accept AVs if they
have the same risk level as human driving (Waycaster et al.,
2018). Specifically, Liu et al. (2019) found that AVs should
be four to five times as safe (i.e., 75–80% reduction in traffic
fatalities) as human drivers, if they are to be tolerated and
widely accepted (Liu et al., 2019). Although safety is a critical
predictor, several other factors influence user perceptions and
behavioral intentions (i.e., trust, perceived usefulness, ease of
use). Advantages and disadvantages are anticipated to arise from
the emergence of AVs. Benefits include improved mobility for
the elderly and disabled (Yang and Coughlin, 2014) and the
liberating of parking spaces for other land uses (Fagnant and
Kockelman, 2015). The potential disadvantages include concerns
relating to privacy, security, insurance, and liability, as well
as job losses (Taeihagh and Lim, 2019). The extent to which
these positive and negative outcomes eventuate will be highly
dependent on user acceptance and adoption of this emerging
technology. Recent AV consumer preference studies, specifically
among older adults, indicate that trust and hesitation are barriers
in adopting full vehicle automation (Reimer, 2014; Hartford,
2015; American Automobile Association, 2016). Faber and van
Lierop (2020) conducted focus groups in which older adults
voiced concerns related to cost, trust, control, and safety of
AVs. A weakness of these studies is that older drivers were
not exposed to “driving” an AV either in real-world format
or via simulator technology. As such, only the perceptions,
and not direct interactive experiences of these participants are
measured, meaning that they do not allow us to fully understand
adoption and acceptance practices of older adults who were
Frontiers in Sustainable Cities | www.frontiersin.org 2June 2020 | Volume 2 | Article 27
Classen et al. Older Drivers’ Experience Automated Vehicles
not exposed to the real life experience of driving or riding in
an AV.
The scientific premise of this study is discussed in the
next five points. (1) The number of older adults is nearing
20% of the population across the US. (2) Driving, a critical
mode of transportation for older adults, yields many health,
community and societal benefits but older drivers are at-
risk for crashes and deleterious crash-related effects. (3) The
deployment of automated shuttles and AVs are expected to
have health and safety benefits for older drivers, positively
impact the environment, and yield societal benefits (i.e.,
improved traffic flow). However, older drivers may not trust
AVs and have additional concerns (i.e., perceived safety) about
vehicle automation. (4) Interactive experiences in AV modes,
in combination with surveys, may more accurately reveal the
thoughts, beliefs, perceptions, or hesitations of older drivers
before, during and after “driving” the automated simulator or
the automated shuttle and (5) inform scientists and engineers
of adjunctive strategies to enhance adoption practices among
older drivers.
Thus, the purpose of this study is to quantify the perceptions
of 69 older drivers, before and after “driving” in an interactive
high-fidelity Realtime Technologies Inc. (RTI) driving simulator
in Level 4 automated mode and riding in the Transdev
operated EasyMile EZ10, Level 4 automated shuttle (Society of
Automotive Engineers International, 2016). We expect that (1)
drivers’ perceptions (intention to use, trust, perceived usefulness,
perceived ease of use, perceived safety, control and driving
efficacy, cost, authority, and social influence), will improve after
being exposed to “driving” the simulator and/or the automated
shuttle and (2) the on-road experience in the shuttle may be a
more positive experience compared to the driving simulator.
Information gained from such experiences will inform
health care professionals, engineers, city managers, and
transportation officials of opportunities and barriers to improve
older drivers’ interaction with AVs, facilitate their ease-of-
use practices, and potentially empower them to adopt these
technologies—and in so doing contribute to congestion
mitigation and crash prevention—core components of a public
health approach. Moreover, because Florida is a model state
for older driver mobility issues (Classen and Awadzi, 2008),
and Gainesville, Florida is an emerging “smart city” (Gonzalez,
2017), it is critical that scientists and engineers study and
understand these adoption patterns of older drivers pertaining
to automated technologies.
MATERIALS AND METHODS
The University of Florida Institutional Review Board approved
the study after a full board review. All participants provided
informed consent for their enrollment into the study. This study
used an experimental crossover-repeated measures design with a
pre-visit survey,intake surveys, exposure to the automated mode
driving simulator or the automated shuttle, post-visit survey 1,
crossover to simulator or automated shuttle, and post-visit survey
2. Participants were recruited through the infrastructure and
support of Oak Hammock and other residential communities,
the older adult recruitment pool of UF’s Institute for Mobility,
Activity and Participation, and through UF’s Institute on Aging.
Participants received $25.00 for participation in the study.
Sixty-nine community dwelling drivers, 65 years of age or
older, from North Central Florida, who had a valid driver’s
license and reported driving within the last 6 months were
included in this study. Participants were excluded if they did
not communicate in English or showed signs of cognitive
impairment, i.e., scoring ≤26 on the Montreal Cognitive
Assessment (MoCA) (Nasreddine et al., 2005). Participant intake
and assessment were conducted in the living areas of the Smart
House in the Oak Hammock Residential Community (5100 S.W.
25th Blvd., Gainesville, FL; see Figure 1), which provided a
comfortable atmosphere for participants and research personnel.
The simulated driving assessments occurred in the simulator
laboratory, located in the garage of the Smart House. The on-road
FIGURE 1 | The Smart House houses the project’s high-fidelity driving simulator and serves as a site for testing.
Frontiers in Sustainable Cities | www.frontiersin.org 3June 2020 | Volume 2 | Article 27
Classen et al. Older Drivers’ Experience Automated Vehicles
experience in the automated shuttle occurred at a formerly used
bus depot in Gainesville, FL.
Equipment
Realtime Technologies Inc. Driving Simulator
The RTI driving simulator is integrated in a full car cab with
seven high definition visual channels, including three forward
channels creating a 180◦field of view, three backward channels
with behind-car views accomplished with one rear screen (seen
through the rearview mirror), two built-in LCD side mirrors,
and one virtual dash display (LCD panel) within the car. The
RTI system has a high fidelity graphic resolution, component
modeling, steering feedback, spatialized audio with realistic
engine, transmission, wind and tire noises, and an autopilot
feature to turn the simulator into automated driving mode (see
Figure 1). The visual display operates at a 60 Hz refresh rate to
support smooth graphics projected on three flat screens with high
intensity projectors. The system allows for experimental drives
with changing environmental conditions, video recording of the
driver’s simulator session, and incorporation of rural, urban,
FIGURE 2 | RTI High Fidelity Simulator with control station.
and highway driving. The simulator operating system drives
are created with a combination of ambient and scripted traffic
that interacts realistically with other vehicles based on human
behavior/decision models and real-time physics-based vehicle
dynamics calculations.
The scenario for this study utilized a 5-min acclimation drive,
to enhance adaptation to the driving simulation environment.
We utilized the simulator sickness questionnaire (Brooks et al.,
2010) to determine pre- and post-drive experiences related to
simulator sickness. The 10-min automated drive (SAE Level 4)
occurred in a low to moderate speed (15–35 mph) residential
and suburban area with realistic road infrastructure, buildings,
and ambient traffic with the system handling all aspects of the
designated driving task. A control area situated at the rear of the
vehicle overlooks the driver, vehicle and screens (see Figure 2)
allowing the operator to control and monitor all aspects of the
experiment. During the simulated scenario, a researcher was
seated in the front passenger seat to assess simulator sickness
via the motion sickness assessment questionnaire (MSAQ)
(Brooks et al., 2010).
EasyMile EZ10 Automated Shuttle
This SAE Level 4 automated shuttle (see Figure 3) uses vision
sensors, light detection, GPS tracking system, and ranging
(LIDAR) to map its environment and to decide upon the best
motion behavior at each instant. The EZ10 shuttle can drive
automated on certain pre-mapped routes but is not yet able to
drive on any road at any time. The shuttle does not have a
steering wheel and can only be manually operated by a joystick
remote control. The maximum speed of the vehicle is 25 miles
per hour. The shuttle has six seats and six standing positions and
can transport up to twelve passengers.
FIGURE 3 | Transdev: EasyMile EZ10 autonomous shuttle (SAE Level 4).
Frontiers in Sustainable Cities | www.frontiersin.org 4June 2020 | Volume 2 | Article 27
Classen et al. Older Drivers’ Experience Automated Vehicles
The shuttle route (see Figure 4) lasted about 10 min and
took place in a deserted bus depot. During testing, participants
remained seated while the shuttle operated at a low speed (≈15
miles per hour) without the presence of ambient traffic or road
users. During segments of the route, the safety operator explained
vehicle capabilities and features to the participants. The number
of participants in the shuttle, during testing, ranged from two to
six participants.
Procedure
Each participant provided written informed consent, was
screened for cognitive impairment using the MoCA, then
completed pencil-and-paper surveys consisting of a demographic
and medical history form, driving habits questionnaire (Owsley
et al., 1999), technology acceptance model (Davis, 1989),
technology readiness index 2.0 (Parasuraman and Colby, 2015),
an autonomous vehicle user perception survey (AVUPS) (Mason
et al., 2020). During participant intake, researchers explained
that both the shuttle and simulator can drive automated pre-
mapped routes but neither vehicle was able to drive on any
road at any time. To minimize the effects of social interaction,
participants were asked to remain silent while riding in the
shuttle and in the driving simulator and to save their questions
for after the experiment. Each participant (N=69), was
randomly assigned to complete the simulator (n=31) or the
automated shuttle (n=38) drive, completed the AVUPS, cross-
over to “drive” the modality not initially driven, and complete
the AVUPS again. After riding in the shuttle or simulator,
each participant completed the Motion Sickness Assessment
Questionnaire (MSAQ).
Simulator Sickness Protocol
Participants driving the simulator may be prone to developing
simulator sickness. We implemented a simulator sickness
protocol to mitigate the occurrence of simulator sickness
(Brooks et al., 2010; Classen et al., 2011). These measures
include: offering dietary recommendations prior to the drive;
utilizing an acclimation protocol; employing a simulator sickness
questionnaire; reducing the sensory incongruence between the
visual, kinesthetic, and vestibular systems by removing visual
clutter in the peripheral field, including engine sounds, and
vibrations for vestibular sensation; supplying environmental
adaptations (5 min acclimation drive, 10 min simulator drive,
cool comfortable conditions at 72 degrees Fahrenheit, air
circulating via fan; avoidance of complex sensory scenes (e.g.,
introduced “calmer” traffic scenes, with some vehicles, a few
pedestrians, a few parked vehicle alongside the road, only
necessary infrastructure, e.g., road marking, speed signs, and
traffic lights); and determining/managing the extent of simulator
sickness symptoms (Stern et al., 2017). All these strategies
areproven to be successful in our previous older adult studies
(Shechtman et al., 2007; Classen et al., 2011).
Measures
Demographic and Medical History Form
The demographic and medical history form was modified from
the National Institute on Aging Clinical Research Toolbox and
used to collect age, gender, race, education, relationship status,
and employment data (US Department of Health & Human
Services, 2019).
Automated Vehicle User Perception Survey (Mason
et al., 2020)
This AVUPS1was used to measure older drivers’ perceptions of
AVs before and after each exposure (simulator and shuttle). The
survey consisted of 4 open-ended items, and 28 visual analog
scale items—ranging from disagree to agree. The scale was a
100 mm horizontal line and participants placed a vertical dash
to signify their level of agreement/disagreement for each item.
Responses were treated as a continuous variable that ranged
from 0 (negative perception of AVS) to 100 (positive perception
of AVs). The survey items represent 11 dimensions including,
experience with technology (3 items; e.g., “I use technology in my
vehicle to make tasks easier for me”), intention to use (3 items;
e.g., “I am open to the idea of using an AV”), trust (4 items; e.g.,
“I am suspicious of an AV”), perceived usefulness (5 items; e.g., “I
believe AVs will allow me to stay active”), perceived ease of use (2
items; e.g., “It will require a lot of effort to figure out how to use
an AV”), perceived safety (3 items; e.g., “I feel safe riding in an
AV”), control/driving efficacy (3 items; e.g., “My driving abilities
will decline due to relying on an AV”), cost (2 items; e.g., “I will
be willing to pay more for an AV compared to what I would pay
for a traditional car”), authority (1 item; “I would use an AV if
National Highway Traffic Safety Administration deems them as
being safe”), media (1 item; “Media portrays AVs in a positive
way”), and social influence (1 item; “My family and friends will
encourage/support me when I use an AV”). Both media and
experience with technology, have been shown to influence users’
perceptions of AVs (Talebian and Mishra, 2018) and AVUPS
items were designed to assess users’ experience with technology
(i.e., not just AV technology) and the media they consumed,
during a longitudinal study.
Item responses were averaged into their respective dimensions
which produced dimension scores ranging from 0 (negative
perceptions of AVs) to 100 (positive perceptions of AVs). The
dimensions’ internal consistency ranged from acceptable (α=
0.75) to excellent (α=0.91). During scale development, the
AVUPS content validity index (CVI) had a rating of 1.00, with 32
of 32 items rated ≥0.86 and a scale CVI of 0.96 (mean CVI of all
items), indicating acceptable content validity (i.e., subject-matter
experts indicated that the items in the survey are representative
of users’ perceptions of AVs). The scale is currently undergoing
further reliability testing and factor analysis.
Motion Sickness Assessment Questionnaire (Brooks
et al., 2010)
The MSAQ questionnaire consisted of 4 items (sweaty, queasy,
dizzy, nauseous) ranging from 0 (not at all) to 7 (severely).
The survey was developed and validated for assessing simulator
sickness symptoms.
1The Automated Vehicle User Perception Survey can be obtained from the
corresponding author: justinmason@phhp.ufl.edu.
Frontiers in Sustainable Cities | www.frontiersin.org 5June 2020 | Volume 2 | Article 27
Classen et al. Older Drivers’ Experience Automated Vehicles
FIGURE 4 | Ten-minute route of the EasyMile EZ10 autonomous shuttle.
For the purposes of this interim analysis, we only focus on the
demographic information, descriptive statistics of simulator and
motion sickness, and all 11 domains from the AVUPS measured
with a visual analog scale.
Data Analysis
Descriptive statistics were conducted on participants’ age, race,
education, and employment status. A series of paired sample
t-tests were performed on older drivers’ simulator/motion
sickness comparing baseline with post-simulator exposure as
well as baseline with post-shuttle exposure. Continuous data
are presented as mean and standard deviation (SD) whereas
categorical data are presented as frequency (%). The 11 domains
(previously described) of AVUPS were used as dependent
variables and were assessed for normality to determine use of
parametric vs. non-parametric analyses via visual examination
(i.e., probability plots, histograms, stem, and leaf plots) and
statistical tests (i.e., Fisher’s skewness and kurtosis and Shapiro-
Wilks tests). A one-way repeated measures ANOVA with three
levels was conducted to assess differences between older drivers’
perceptions at baseline, after exposure to the simulator, and
after exposure to the automated shuttle. A one-way repeated
measures ANOVA with three levels was performed to assess
differences between older drivers’ perceptions at baseline, after
being exposed to one AV technology (i.e., post-exposure 1),
and after being exposed to both AV technologies (i.e., post-
exposure 2). Post-hoc tests (i.e., paired t-tests), were performed
if repeated measures ANOVAs reached significance (p<
0.05). No adjustments were made for multiple comparisons
(Rothman, 1990). Study data were collected and managed using
Research Electronic Data Capture (REDCap) hosted at the
UF (Harris et al., 2019). R Studios and R version 3.6.1 (R
Core Team, 2019) were used for data collation and analyses.
Significance level was set at α=0.05 with an accompanying 95%
confidence level.
RESULTS
A total of 69 participants (mean age =74.64, SD =6.17),
consisting of 29 males (mean age =77.03, SD =5.42) and 40
females (mean age =72.90, SD =6.16) completed our study.
Twenty females and 18 males were first exposed to the shuttle
(n=38), whereas 20 females and 11 males were first exposed
to the simulator (n=31). The racial distribution indicated that
participants were self-identifying as 60 (87%) White, 6 (9%)
Black, and 3 (4%) Other. The study participants demonstrated
a high level of education as 73% had either a doctorate (26%),
master’s (28%) or bachelor’s degree (19%); whereas 26% had an
associate, some college or a technical school certification, and
1% had either a GED or high school education. Participants
reported their current employment status, 58 (84%) retired, 10
(14.5%) working part-time, and 1 (1%) working full-time. All
participants were able to complete their ride in the shuttle and
driving simulator.
The paired-sample t-tests for simulator sickness revealed
differences for queasy, dizzy, and nauseous after experiencing the
driving simulator compared to baseline. Older drivers’ ratings
of feeling queasy, dizzy, and nauseous increased after riding
in the simulator compared to baseline. The paired-sample t-
tests for motion sickness revealed differences for feeling sweaty
after riding in the automated shuttle compared to baseline.
Older drivers’ ratings of feeling sweaty decreased after riding in
the automated shuttle. Motion sickness and simulator sickness
results are displayed in Table 1.
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Classen et al. Older Drivers’ Experience Automated Vehicles
TABLE 1 | Older drivers’ simulator and motion sickness before and after being exposed to the automated shuttle and simulator.
Dimensions Pre-exposure Post-exposure Statistics P
M(SD) Range M(SD) Range
SIMULATOR
Sweaty 0.00 (0.00) 0 0.19 (0.85) 0–6 t(68) =1.85 0.068
Queasy 0.04 (0.36) 0–3 0.68 (1.31) 0–7 t(68) =4.66 <0.001
Dizzy 0.04 (0.21) 0–1 0.54 (1.20) 0–5 t(68) =3.53 0.001
Nauseous 0.01 (0.12) 0–1 0.25 (0.78) 0–5 t(68) =2.80 0.007
SHUTTLE
Sweaty 0.49 (1.22) 0–5 0.13 (0.66) 0–5 t(68) =2.23 0.029
Queasy 0.07 (0.60) 0–5 0.00 (0.00) 0 t(68) =1.00 0.321
Dizzy 0.00 (0.00) 0 0.00 (0.00) 0
Nauseous 0.04 (0.27) 0–2 0.04 (0.27) 0–2 t(68) =0.00 1.0
The motion/simulator sickness questionnaire dimensions ranged from 0 to 7. Range is displayed as minimum to maximum.
Older Drivers’ Perceptions Before,
Post-shuttle, and Post-simulator
The repeated measures ANOVA revealed differences between
exposure to AV technologies for drivers’ intention to use,F(2,136)
=3.360, p=0.038, ηp2=0.047, trust,F(2,136) =13.565, p<0.001,
ηp2=0.166, perceived usefulness,F(2,136) =5.018, p=0.008, ηp2
=0.069, perceived safety,F(2,136) =11.140, p<0.001, ηp2=0.141,
and control and driving efficacy,F(2,136) =3.724, p=0.027, ηp2=
0.052. However, older drivers’ intention to use was not statistically
significant after being exposed to the simulator (p=0.076) and
shuttle (p=1.00) compared to baseline. Older drivers’ trust was
enhanced after being exposed to the simulator (p=0.013) and
shuttle (p<0.001) compared to baseline. Older drivers’ perceived
usefulness (p=0.005) was enhanced after the shuttle compared
to baseline. Older drivers’ perceived safety was enhanced after
being exposed to the simulator (p=0.006) and shuttle (p<
0.001) compared to baseline. Older drivers’ control and driving
efficacy were enhanced after riding in the shuttle compared to
after the simulator (p=0.043, Cohen’s d=0.30). The repeated
measures ANOVA revealed no significant differences for older
drivers’ experience with technology,F(1.8,123.5) =0.166, p=0.827,
ηp2=0.002, perceived ease of use, F(2,136) =0.172, p=0.842,
ηp2=0.003, cost,F(2,136) =2.838, p=0.062, ηp2=0.040,
authority, F(2,136) =1.598, p=0.206, ηp2=0.023, media, F(2,136)
=1.773, p=0.174, ηp2=0.025, or social influences,F(2,136) =
1.364, p=0.259, ηp2=0.020. Table 2 indicates the descriptive
statistics from the repeated measures ANOVA comparing older
drivers’ perceptions at baseline and after being exposed to the
simulator and automated shuttle. The bar graphs (Figure 5)
display descriptive trends for the AVUPS domains at baseline,
after the shuttle, and after the simulator.
Older Drivers’ Perceptions Before,
Post-exposure 1, and Post-exposure 2
The repeated measures ANOVA revealed differences between
the number of exposures (i.e., baseline, post-exposure 1, post-
exposure 2) to AV technologies for older drivers’ trust,F(2,136)
=13.565, p<0.001, ηp2=0.168, perceived usefulness,F(2,136)
=3.360, p=0.038, ηp2=0.057, and perceived safety,F(2,136) =
13.565, p<0.001, η2=0.148. Older drivers’ trust was enhanced
at post-exposure 1 (p=0.010) and post-exposure 2 (p<0.001)
compared to baseline. Older drivers’ perceived usefulness (p=
0.028) and cost (p=0.029) increased after being exposed to
both forms of AV technology. The repeated measures ANOVA
revealed no significant differences for older drivers’ experience
with technology,F(1.8,124.2) =0.074, p=0.915, ηp2=0.001,
intention to use, F(2,136) =2.761, p=0.067, ηp2=0.039, perceived
ease of use, F(2,136) =0.185, p=0.832, ηp2=0.003, control
and driving efficacy, F(2,136) =0.332, p=0.718, ηp2=0.005,
cost, F(2,136) =2.784, p=0.065, ηp2=0.039, authority, F(2,136)
=1.200, p=0.304, ηp2=0.017, media, F(2,136) =0.837, p=
0.435, ηp2=0.012, or social influences, F(2,136) =1.747, p=
0.178, ηp2=0.025. Older drivers’ perceived safety increased at
post-exposure 1 (p=0.013) and post-exposure 2 (p<0.001)
compared to baseline. Table 3 indicates the descriptive statistics
from the repeated measures ANOVA comparing older drivers’
perceptions at baseline, post-exposure 1, and post-exposure 2.
DISCUSSION
This interim analysis was conducted to quantify the perceptions
of 69 older drivers, who have been exposed to “driving” the
interactive high-fidelity RTI driving simulator and riding in the
Transdev manufactured EasyMile EZ10 automated shuttle.
We had two expectations: Expectation 1: Older drivers’
perceptions in 9 of the 11 domains (all except for media and
experience with technology) will change after being exposed
to riding in the simulator and/or the automated shuttle.
This was indeed the case for 3 of the 9 domains indicating
older drivers’ perceptions of AVs. Specifically, older drivers’
trust and perceived safety increased after being exposed to
either the simulator or shuttle whereas older drivers’ perceived
usefulness increased after begin exposed to the automated
shuttle. Older drivers’ trust and perceived safety increased
after their first exposure to AVs (regardless of whether it
was the simulator or shuttle) whereas trust, perceived safety,
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Classen et al. Older Drivers’ Experience Automated Vehicles
TABLE 2 | Older drivers’ perceptions at baseline, post-simulator, and post-shuttle.
Dimensions Baseline Simulator Shuttle Cohen’s d
Experience with technology 78.2 (16.3) 78.0 (18.0) 79.1 (15.8) 0.07
Intention to use 75.7 (18.8) 80.6 (18.9) 76.6 (18.0) −0.22
Trust 64.1 (19.8) 70.3 (18.5)* 74.3 (16.6)* 0.26
Perceived usefulness 75.5 (16.6) 78.9 (19.8) 81.5 (15.4)* 0.15
Perceived ease of use 75.4 (19.8) 76.6 (20.0) 76.7 (19.3) 0.01
Perceived safety 69.4 (19.9) 76.6 (17.9)* 78.74 (16.7)* 0.13
Control/Driving efficacy 45.9 (18.8) 44.5 (18.4) 50.38 (20.2) 0.30†
Cost 63.9 (21.8) 67.1 (21.6) 69.0 (19.0) 0.09
Authority 76.5 (24.7) 78.4 (22.9) 81.0 (18.3) 0.13
Media 60.0 (23.2) 59.6 (24.2) 64.6 (20.5) 0.22
Social Influence 69.7 (25.1) 70.8 (24.5) 74.2 (19.3) 0.16
Cohen’s d compares older drivers’ perceptions after exposure to the simulator and automated shuttle. *p<0.05 signifies differences compared to baseline. †p<0.05 signifies differences
of perceptions after the simulator compared to after the shuttle. Dimension scores range from 0 to 100.
FIGURE 5 | Bar graph for older drivers’ perception of AVs, via a visual analog scale, at baseline, post-simulator, and post-shuttle.
perceived usefulness, and cost increased after being exposed
to both modes of AV technology. Expectation 2: The on-
road experience in the automated shuttle may increase drivers’
perceptions of AV technology compared to the driving simulator.
Interestingly, when comparing perceptions after the simulator
vs. after the shuttle, only control and driving efficacy, reached
statistical significance. It is possible that older drivers’ on-
road experience was more realistic compared to the driving
simulation, thus increasing their perceived control of AVs
after riding in the automated shuttle. The on-board engineer,
remote control to take over control of the automated shuttle,
and accessible control panel may have influenced their sense
of control.
Older drivers did not experience motion sickness in the
automated shuttle, which may have influenced their perceptions
compared to their experience in the driving simulator, resulting
in increased simulator sickness severity. All participants
completed the driving simulation, which suggests that feelings
of simulator sickness were manageable. The lack of motion
sickness experienced by older adults in the automated shuttle is
promising for AV acceptance and adoption. Although, further
investigation is required to discern differences between simulator
sickness and motion sickness. Specifically, researchers should
strive to develop congruent routes, settings, and vehicle speeds
when comparing sickness that occurs due to vehicle automation
or driving simulation. Another interesting finding is that the
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Classen et al. Older Drivers’ Experience Automated Vehicles
TABLE 3 | Older drivers’ perceptions at baseline, post-exposure 1, and post-exposure 2.
Dimensions Baseline Post-exposure 1 Post-exposure 2 Cohen’s d
Experience with technology 78.2 (16.3) 78.2 (18.0) 78.9 (15.8) 0.04
Intention to use 75.7 (18.8) 77.0 (19.3) 80.3 (17.7) 0.18
Trust 64.1 (19.8) 70.2 (19.7)* 74.4 (15.3)* 0.24
Perceived usefulness 75.5 (16.6) 79.8 (18.2) 80.6 (17.3)* 0.05
Perceived ease of use 75.4 (19.8) 76.5 (20.9) 76.9 (18.4) 0.02
Perceived safety 69.4 (19.9) 76.1 (18.3)* 79.2 (16.2)* 0.18
Control/Driving efficacy 45.9 (18.8) 47.3 (22.2) 47.6 (16.5) 0.02
Cost 63.9 (21.8) 67.2 (19.6) 68.9 (21.0)* 0.09
Authority 76.5 (24.7) 79.0 (20.4) 80.4 (21.1) 0.07
Media 60.0 (23.2) 60.6 (23.3) 63.6 (21.7) 0.13
Social influence 70.0 (25.1) 69.4 (25.0) 74.3 (20.2) 0.22
Cohen’s d compares older drivers’ perceptions between their first (i.e., post-exposure 1) and second exposure (i.e., post-exposure 2) to automated vehicles. *p<0.05 signifies
differences compared to baseline. Dimension scores range from 0 to 100.
older drivers’ perceptions of perceived usefulness and cost, were
enhanced after riding in both modes of vehicle automation
compared to baseline whereas these perceptions did not change
after their first AV exposure. One potential reason may be that
the participants experience in the simulator and shuttle provided
them with more thorough understanding of vehicle automation.
Older drivers may need to be exposed to different modes of
vehicle automation or be exposed to AVs on multiple occasions.
However, these interpretations needs to occur with caution as
Type 1 (i.e., due to multiple comparisons) and/or Type II error
(i.e., inadequate power to detect a true difference) may be evident
in this interim analysis that only accounts for the perceptions of
69 older drivers.
Descriptively, all 11 domains increased after being exposed
to the automated shuttle compared to baseline (see Figure 5).
Furthermore, participants reported more positive perceptions
for 10 domains (all except for intention to use), after being
exposed to the automated shuttle compared to the driving
simulator. Older drivers’ perceptions descriptively increased after
their first exposure (i.e., regardless of it was the simulator
or shuttle) to AV technology and continued to increase
after being exposed to both modes of AV technology. Older
drivers in the current study were exposed to the simulator
and shuttle, which operated in very predictable conditions
with a closed route, during clear environmental conditions,
with clear road markings, and few interferences. Results from
the current study align with similar studies where older
drivers were exposed to a SAE Level 4 automated shuttle
(Eden et al., 2017; Nordhoff et al., 2019) and a Level 4
driving simulator (Li et al., 2019) during ideal circumstances.
However, as seen in findings from Walker et al. (2018),
drivers that have negative interactions with AVs have decreased
perceptions (i.e., trust) of AVs. Thus, drivers should be exposed
to scenarios and routes that realistically portray automated
system capabilities and limitations. Furthermore, the negative
interactions with on-road vehicle automation may decrease
perceptions of AVs at a greater magnitude than being exposed
to driving simulation.
The results of this study align with findings from Penmetsa
et al. (2019), indicating that as the public increasingly interacts
with AVs, their attitudes toward the technology are more
likely to be positive. Similar to Penmetsa et al. (2019), we
recommend that policy makers provide opportunity for the
public to interact with AVs. The interactive experience with
AVs, ideally on public roads may increase the acceptance
and adoption of AV technology. Numerous limitations in this
study occurred due to the restrictions imposed by the federal
government, which delayed our study timeline and required
amendments to our research design and protocol. For example,
the National Highway Traffic Safety Administration (NHTSA)
issued a waiver approving pilot testing of participants, but the
shuttle could not operate on public roads. As such, we modified
our original plan—and operated the shuttle, not on a congruent
road course to that in the simulator—but in a deserted bus
depot, where there was no traffic, road users or road signs. The
differences (i.e., shuttle speed, road-type, and traffic) between
the shuttle route and simulator scenario must be noted as a
limitation to this study. The physics experienced during driving
simulation affects cognitive awareness that there would be no real
consequences in the case of an adverse event, such as a crash.
Moreover, the automated shuttle traveled at a restricted speed
of 15 miles per hour, whereas the driving simulator scenario
had speeds ranging from 15 to 35 miles per hour. Of course,
this arrangement may have impacted the perceptions of the
older drivers—and we may see a resulting effect following this
arrangement. That means that the study may be prone to bias
and an underestimation or overestimation of the actual effects
of the exposure, especially pertaining to the experiences in the
automated shuttle. Furthermore, the NHTSA waiver that was
issued, expired after 6 months, and the study team is still awaiting
permission to continue with the participant testing—hence the
interim analysis. Lastly, in February 2020, NHTSA ordered a
nationwide suspension affecting all 16 operating EZ10 automated
shuttles. An EZ10 automated shuttle operating in Columbus,
Ohio traveling 7 miles per hour made an emergency stop which
caused a passenger to fall from their seat in a “minor incident”
Frontiers in Sustainable Cities | www.frontiersin.org 9June 2020 | Volume 2 | Article 27
Classen et al. Older Drivers’ Experience Automated Vehicles
(EasyMile, 2020). Finally, participants in our study were mainly
white and highly educated, which may have caused sampling
bias—as the views of minorities and less educated groups were
not adequately represented.
This study is one of the first that actually presents data
on user perceptions after participants have been exposed to
two forms of vehicle automation (i.e., simulator and automated
shuttle). The findings of this interim analysis suggest that the
simulator, when programmed to run in the SAE Level 4 mode
of autonomy, can be used to gauge user experiences of being
exposed to autonomous technology. However, the participants’
experiences showed a greater increase in perception pertaining
to especially trust and perceived safety, when exposed to the
automated shuttle. This suggest that the automated shuttle may
be a superior mode of automation to influence user acceptance.
However, the simulator may also influence users’ perceptions
and be a viable training device to provide specific scenarios that
highlight capabilities and limitations of vehicle automation. For
example, in cities where automated shuttles are not a reality
yet, the autonomous simulator may be used as a reasonable
substitute to gauge user experience and potential adoption of the
AV technology.
Next steps for this project are to increase and balance (i.e.,
match for gender and age) the study sample size, by collecting and
analyzing the perceptions of the total sample (N=106). A robust
analysis of the complete data set will include linear modeling
as well as repeated measure ANOVAs to explore gender, age,
and group by time (i.e., order) interactions. Additionally, we
will analyze drivers’ qualitative feedback, given in the four
open ended questions of the AVUPS, to better understand their
interactive experience pertaining to the acceptance and adoption
of AVs. Information from this completed study will be used to
develop strategies to further improve upon older driver adoption
practices of AV, suggest practical hints to engineers for design
elements, and provide information to shape city and state policies
for regulatory purposes of AV deployment, adoption and use.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by University of Florida Institutional Review Board.
The patients/participants provided their written informed
consent to participate in this study.
AUTHOR CONTRIBUTIONS
SC and VS: study conception and design. SC, JR, JW, and JM:
develop driving simulation. JW and JM: data collection. JM and
SC: analysis and interpretation of results. SC, JM, JW, and VS:
draft manuscript preparation. All authors reviewed the results
and approved the final version of the manuscript.
FUNDING
This research project (#69A3551747104) was funded through
the US Department of Transportation and the Southeastern
Transportation Research, Innovation, Development, and
Education Center.
ACKNOWLEDGMENTS
The Institute for Mobility, Activity, and Participation provided
infrastructure and support for this study.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Classen, Mason, Wersal, Sisiopiku and Rogers. This is an open-
access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
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Frontiers in Sustainable Cities | www.frontiersin.org 12 June 2020 | Volume 2 | Article 27
