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Travel Safety and Technology Adoption by Elderly Populations
Dustin J. Souders, M.S.*
Neil Charness, Ph.D.*
*Department of Psychology, Florida State University, Tallahassee, FL
Prepared for the Florida Automated Vehicles Summit, December 15-16, 2014
December 1, 2014
ABSTRACT: This literature review focuses on the aging population’s driving performance and
factors determining their willingness to adopt various types of Advanced Driving Assistance
Systems (ADAS) and Automated Vehicle (AV) technologies. Through a user-centered approach,
we outline safety issues associated with age-related declines in driving ability, problems that may
arise when aging adults’ mobility is reduced after they cease driving, and how these emerging
technologies may help aging adults faced with driving cessation avoid these deleterious
consequences. We review pertinent literature on: 1) what age-related cognitive and sensory
deficits affect driving performance and older adult compensation strategies for them; 2) crash
scenarios older adults (OAs) are overrepresented in; 3) how ADAS and AV technologies can be
employed to safely allow OAs to enjoy the mobility benefits of a personal vehicle; and 4) factors
affecting older drivers’ adoption of these technologies. Suggestions for maintaining OA mobility
through the use of emergent ADAS as well as their implications for future AV technologies are
provided.
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INTRODUCTION
This literature review adopts a user-centered approach focused on the older driver (age 65+) to
provide insight for safely maintaining aging individuals’ mobility by helping them stay behind
the wheel of their own personal vehicle through the use of emerging ADAS and AV
technologies. First, an overview of demographic shifts, age-related factors contributing to driving
cessation, and older drivers’ compensation strategies for various sensory, cognitive, and physical
declines are provided. Second, factors affecting the adoption of ADAS and AV technologies are
discussed. Finally, crash scenarios that older drivers are overrepresented in are reviewed, and
possible ADAS/AV solutions for these crash types are identified.
PART ONE: OVERVIEW OF AGING DRIVERS & DRIVING CESSATION
Issues Surrounding the Increase of Older Drivers on the Road
As of 2010, approximately 40 million (13%) of the U.S. population was over the age of 65, and
by 2030, it is estimated that 72 million (19%) of the U.S. population will be older than 65
(United Nations, 2011). Individuals aged 65+ years represent the largest growing age group in
the driving population, and are keeping their licenses longer and driving more miles per licensed
driver than previous older generations (Lyman, Ferguson, Braver, & Williams, 2002). This
increase in the proportion of older drivers on the road implies that policy makers should
familiarize themselves with this population’s functional limitations associated with driving and
possible methods of intervention to promote safe mobility for life. In particular, technologies
such as advanced driving assistance systems (ADAS) and automated vehicles may provide
solutions to the mobility challenge. (See Reimer, 2014 for an excellent review of the varying
levels of automation and its implications on older adult safety and mobility). Age-related
functional limitations such as deteriorations in vision, cognitive skills (e.g., processing speed and
memory ability), and motor skills place these older drivers at increased risk of motor vehicle
crashes (MVCs; Stutts, Stewart, & Martell, 1998). This population’s increased physical frailty
due to changes in bone composition (Chavassieux, Seeman, & Delmas, 2007) also leaves them
more susceptible to injury even in minor MVCs (Cunningham, Howard, Walsh, Coakley, &
O'Neill, 2001). This can lead to them having more surgical, medical, and therapy workloads
before being discharged from the hospital, and significantly more complications, as well as
significantly longer hospital stays.
Factors Leading to Decreasing Fitness to Drive, Driving Cessation, & Associated Outcomes
Aging brings about changes in sensory, cognitive, and physical abilities that can affect driving
safety. Sensory issues include decreases in visual acuity and hearing (Ivers, Mitchell, &
Cumming, 1999; Marottoli et al., 1998), though hearing deficits do not seem to impact driving
performance as much (Anstey, Wood, Lord, & Walker, 2005). Cognitive risk factors include
behavioral slowing (Salthouse, 2010), decreases in selective visual attention (Baldock, Mathias,
McLean, & Berndt, 2007), and decreases in the ability to perform planned actions under time
pressures (Stelmach & Nahom, 1992), all of which may lead to decreasing fitness to drive.
Physical problems such as arthritis also can have an impact on driving performance as poor neck
rotation has been found to double the risk of a crash (Marottoli et al., 1998). Many chronic
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diseases associated with aging are treated with prescription drugs and over the counter
medications that can also affect driving performance (Hetland & Carr, 2014).
Access to some form of transportation is critical to older adults’ maintaining their health, social
inclusion, and independence in the later years of life (O’Neill & Carr, 2006). The decision to
stop driving often comes at a time in older adults’ lives in which they have less disposable
income, and neurological disease, cataracts, decreased physical activity, and/or functional
disability (Marottoli et al., 1993). Though with age, some have argued that many older adults are
increasingly able to purchase a new vehicle to meet changing transportation needs (Coughlin,
2009). Due to a high variability in income in the older population, there will be older adults that
have sufficient financial resources to buy vehicles equipped with assistive technologies
themselves, while others in this age range might need public subsidies in order to do so. Driving
cessation in those over the age of 65 is associated with reduced quality of life and psychological
well-being (Adler & Rottunda, 2006; Gruber, Mosimann, Müri, & Nef, 2013; Whelan, Langford,
Oxley, Koppel, & Charlton, 2006). In fact, depressive symptoms have been found to worsen in
older adults that have ceased driving, or have lessened their amount of driving (Fonda, Wallace,
& Herzog, 2001).
Older Drivers’ Compensation Strategies
Older drivers are often aware of their declining ability to drive and compensate in a number of
ways. Michon's (1985) hierarchical structure of the driving task consists of three task levels: the
strategic level (decisions made before getting behind the wheel), the tactical level (operating
heuristics once behind the wheel), and the operational level (actual driving behavior). Decisions
to take familiar routes, or avoid driving at night, during rush hour, or poor weather are strategic
level choices often made by older drivers. Maintaining a certain speed or headway while driving
represents a tactical level choice. Older drivers tend to make decisions at the strategic and
tactical levels to compensate for their deficits, which provide them with more time to react at the
operational level. Older adults’ driving behavior might slightly benefit from their being less
inclined to multi-task behind the wheel due to difficulties in sharing attention (Brouwer,
Rothengatter, & Van Wolffelaar, 1992), but it is important to note that shared attention demands
should be minimized in any interface design (Davidse, 2006).
PART TWO: FACTORS INFLUENCING OLDER DRIVER ADOPTION OF ADAS/AV
TECHNOLOGY
Older Adults’ Adoption of Technology
Many models of technology adoption follow a cost-benefit evaluation framework. One of the
most influential is the Unified Theory of Acceptance and Use of Technology (UTAUT2)
(Venkatesh, Thong, & Xu, 2012). The major cost factors in UTAUT2 are effort expectancy
(perceived ease of use) and price value, whereas major benefits include performance expectancy
(perceived usefulness) and hedonic motivation. Other important factors are social influences,
habit, and facilitating conditions such as expectations about support. Demographic predictors
typically include age, gender, and technology experience. In a recent review of OA adoption of
technology, Lee and Coughlin (2014) identified ten factors (value, usability, affordability,
accessibility, technical support, social support, emotion, independence, experience, and
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confidence) as facilitators or determinants of OA technology adoption that have many parallels
to UTAUT2, with the addition of distinct factors for affordability (i.e., perception of initial
financial costs vs. immediacy and clarity of possible gains after purchase) and independence
(i.e., preventing stigma and protecting autonomy). Of these factors, value, usability, social
support, emotion, and confidence seem to be the most likely to positively affect OA adoption of
ADAS/AV technology while affordability and accessibility seem to be the most limiting at
present. The effects of experience, technical support, and independence on OA adoption of
ADAS/AV technologies seem harder to gauge. The public has little prior experience with these
emergent technologies, the form and content of these technologies’ technical support is still
being constructed, and it is too early to ascertain the nature of OAs’ self-perceptions when using
ADAS/AV technologies.
Another general framework for technology adoption and use that reflects a demand–capability
framework is provided in Figure 1.
Figure 1. Technology Adoption and Use Model (adapted from Charness & Boot, accepted)
Motivation and attitudes influence the goals that people set about technology adoption and goals
are evaluated in the context of someone’s perceptual, cognitive, and psychomotor capabilities.
On the technology side, the potential demands that technology make on the potential user
(perceived ease of use), coupled with the perceived benefits, and dollar cost would be expected
to be weighed within the benefit/cost evaluation cycle. Experience with technology can feed
back to influence motivation and attitudes, but also someone’s capabilities (e.g., a hearing aid
could facilitate interactions with smartphones that provide auditory alerts).
In the case of automated vehicle technology and older adults, we would expect perceived ease of
use, price value, and perceived usefulness to dominate decision-making. When expected value,
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such as enablement of mobility, increases, perhaps through fears of loss of license or being able
to drive safely, there should be a strong motivation to seek out automated vehicles. Fully
autonomous vehicles could probably provide the greatest benefit for those who are no longer
competent to drive, but, there is a great deal of uncertainty about how much older adults would
be willing to pay for automated vehicle technology. Our only indications are based on prior
studies of willingness to use technology to help with tasks needed for maintaining independence
(Schulz et al., 2014). Kitchen (e.g., meal preparation and washing dishes) and personal care
(getting in and out of bed, dressing and toileting) assistance was only worth about $40-45 per
month to baby boomers aged 45-64, for those indicating some willingness to pay. About a third
of the sample was unwilling to pay anything for these services.
Conceptualizing Trust in AV Systems
One potential barrier to adoption of technology-based systems is trust that the technology will
work as intended. Hoff and Bashir (2013) proposed a theoretical model for trust in automated
systems that broke trust into three components that provide a useful lens to view the complex
case of older adult adoption of AV technology:
1. Dispositional Trust: variability of individuals’ instinctive tendencies to trust automation
that cannot be changed in the short term. Dispositional trust is made up of culture, age,
gender, and personality traits.
2. Situational Trust: varies depending on the specific context of an interaction and is made
up of environmental variability (i.e., type of system, system complexity, type of task,
perceived risks/benefits, framing of task, physical environment, organizational factors)
and context-independent user variability (i.e., self-confidence, subject matter expertise,
mental well-being).
3. Learned Trust: based on past experiences of a user relevant to a specific automated
system and varies depending on the characteristics of a system. Learned trust is further
divided into the user’s trust prior to using the system (initial learned trust) and the user’s
trust while operating the system (dynamic learned trust).
Using this framework, it makes sense that older adults faced with the decision to cease driving
may have higher levels of situational trust than those that remain confident in their driving
abilities. The largest gains to be made in trust are in older driver’s learned trust of AV
technology, which will only come with increasing news of these systems’ efficacy and/or
positive experience using these technologies once they are available.
PART THREE: OLDER DRIVER CRASH SCENARIOS & POSSIBLE SOLUTIONS
Older Drivers’ Common Crash Scenarios
Older adults are more likely to get into certain crash types, often involving cross-traffic (left in
the U.S.) turns at intersections (Alam & Spainhour, 2009; Hakamies-Blomqvist, 1994; Keskinen,
Hiro, & Katila, 1998; McGwin & Brown, 1999; Preusser, Williams, Ferguson, Ulmer, &
Weinstein, 1998). Alam and Spainhour (2009) analyzed data from the year 2000 and found that
older drivers found to be at fault in fatal intersection crashes in Florida typically misjudged the
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speeds of other vehicles, failed to observe other vehicles, disregarded traffic signals, or made
disallowed left turns. Older drivers were overrepresented in left turn crashes versus oncoming
and cross traffic, and this represented 42% of the crashes in which older drivers were at fault.
Interestingly, older drivers were more at risk of being hit, as opposed to hitting other drivers
(Hakamies-Blomqvist, 1994). This is reflective of their relatively poor ability to gauge oncoming
traffic’s speed to traverse intersections and in their relative lack of non-intersection crashes
(Alam & Spainhour, 2009).
Possible ADAS/AV Solutions
For a concise, yet comprehensive review of older driver weaknesses and the type of assistance
needed to help overcome these weaknesses, we refer the reader to Table 1 from Davidse (2006).
In the current report, we adapt this table to show which weaknesses that are most relevant to
safety can be supported by ADAS/AV technologies.
Age-Related Weakness
Driving Related Difficulty
Assistance Needed
Peripheral vision
Merging or changing lanes
without heed to other road users
Signaling objects in driver's
blind spot
Motion perception
Correctly judging the movement
of other road users and their
approach speed
Draw attention to oncoming
traffic
Selective attention
Overlooking traffic signs and
signals
Cue relevant information to
driver
Speed of
processing/Making
decisions
Complex traffic scenarios lead to
longer reaction time
Provide warning for
upcoming complex traffic
scenarios
Head/Neck Flexibility
Merging or changing lanes
without heed to other road users
Signaling objects in driver's
blind spot
Performance under
time pressure
suboptimal decisions
Provide warning for
upcoming complex traffic
scenarios
Table adapted from Davidse (2006).
Because older drivers are more likely to be collided with by other drivers rather than initiating
the collision themselves (Hakamies-Blomqvist, 1994), it does not appear that the actual
execution of the driving task is what needs bolstering, but rather systems that help older drivers
make accurate judgments at the tactical level, with enough time to execute the correct situational
action at the operational level. With this in mind, and from the information presented in the table,
it is clear that the most useful assistive devices will draw attention to approaching traffic, signal
obstructions in the driver’s blind spot, direct attention to relevant information and signage,
and/or provide advance knowledge on the upcoming traffic situation. ADAS can compensate for
decreased peripheral vision caused by visual declines and decreases in the neck’s range of
motion (Klein, 1991; Shinar & Schieber, 1991) by alerting the driver to objects in their blind spot
and helping them avoid colliding with other drivers when merging or changing lanes. Declines in
vision and hearing that lead to older drivers having difficulty in motion perception and errors in
judging the movement or approach speed of other motorists can be compensated for by ADAS
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drawing attention to approaching traffic. Declines in selective attention and speed of information
processing/decision-making (Brouwer, Waterink, Van Wolffelaar, & Rothengatter, 1991;
Quilter, Giambra, & Benson, 1983) can cause older drivers to overlook pertinent traffic signs and
signals and keep them from performing necessary actions in a timely manner. ADAS may be
used to direct the older drivers’ attention to relevant information and provide prior knowledge on
the upcoming traffic situation to allow the older driver more time to initiate the correct action.
Advanced Driver Assistance Systems & Studies of their Efficacy and Adoption
The timeline to deployment of fully autonomous vehicles is a subject of speculation and debate
(Saffo & Bergbaum, 2013). In the interim, advanced driver assistance systems (ADAS) that aid
in certain driving tasks are being successfully developed and sold in many new cars, particularly
in luxury brand vehicles. This section reviews different types of ADAS and how they can help
older drivers, their availability in the marketplace, and the results of any studies looking at their
use and/or adoption by older drivers.
Collision Warning Systems
Collision warning systems that draw the older driver’s attention to oncoming traffic in
intersections would be the most useful, and would help older drivers successfully make left turns
(Davidse, 2006). Mitchell and Suen (1997) note that the complexity of analyzing collision
avoidance in intersections might lead to this form of ADAS taking longer to successfully
develop. (Oxley & Mitchell, 1995)simulated such a system that gave older drivers a green light
whenever there was a gap in oncoming traffic of at least 6 seconds in which they could execute a
left turn while stopped at an intersection. All older participants reported that this system would
be “useful” or “very useful” while driving at night, while only 63% thought the same during the
day and only about half of the older participants reported willingness to pay for the system.
Results showed more near-misses when older drivers used the system, and Oxley (1996)later
cautioned against using uniform settings, but instead suggested the gap be adjustable to match
the individual driver’s characteristics, such as reaction time.
Lane Changing/Merging
While fully automated lane-changing systems have been expected to be developed within the
next 20 years (Mitchell & Suen, 1997),only lane-change collision warning systems are currently
available (Regan, Oxley, Godley, & Tingvall, 2001). These systems have not been evaluated
with older drivers (Davidse, 2006). Inherent drawbacks in lane-change collision warning systems
such as high false alarm rates and small windows for course-correction (in both physical space as
well as time to execute) after the system has alerted the driver suggest that these lane-
changing/merging assistance systems may not benefit older drivers until automation is fully
incorporated.
Blind Spot and Obstacle Detection
Most useful in preventing low-speed crashes that may occur while parking, blind spot and
obstacle detection systems most likely will not have a large effect on the overall road safety of
older adults (Davidse, 2006). Interestingly, when two types of reversing aids were tested in a
simulator by Oxley and Mitchell (1995), they enabled older drivers to park closer to objects and
hence fit in to smaller spaces. Vehicle entry and egress often poses a difficulty for older adults,
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and leads the list of problem areas in vehicle design for the older driver (Herriotts, 2005). This
might explain why Oxley and Mitchell (1995) found that most of the older drivers in their
simulator study not only found such a system useful and easy to use, but they were even willing
to pay market-price for it. Blind spot and obstacle detection systems therefore might keep older
adults driving their own vehicles longer more by increasing their confidence in common,
previously low-confidence driving scenarios (in this case, parking and getting in and out of the
car) as opposed to substantially increasing their safety in more dangerous crashes. Comfort (as
well as safety) is likely to be an important consideration in driving cessation.
In-vehicle Signing Information Systems
Older drivers’ difficulties in selective attention while driving may be helped by in-vehicle
signing information systems (ISIS) that make use of heads-up displays to highlight the next
important traffic sign in a scene. However, it is important to note that these in-vehicle displays
can shift significant amounts of the driver’s attention away from the roadway that would
otherwise be used for environmental scanning (J. D. Lee, 1997). Incorporation of ISIS should
display easily decipherable warnings in ways that maximize the amount of attention the driver
has on the roadway.
Intelligent Cruise Control
Intelligent cruise control (ICC), also known as adaptive cruise control (ACC), adds a distance
keeping function to the speed-keeping ability of normal cruise control. Mitchell and Suen (1997)
envisioned an ICC system for older adults that took cues from the road into account, similar to
ISIS, such as the speed limit, yield signs, stoplights, and railroad crossings. In a questionnaire
study that surveyed ACC users, Larsson (2012) found that the longer drivers had used their ACC
systems, the more aware they became of its limitations. Most users reported that the ACC forced
them to take control intermittently, which discouraged full reliance on the ACC. This suggests
that the previously discussed potential for gains in learned trust with repeated usage of AV
technology is indeed present in this semi-automated system, though it does imply that
intermittent use is necessary for this learning to occur.
CONCLUSION
We have outlined how automated vehicle technology may assist older drivers to maintain safe
mobility for life. ADAS and AV have the potential to improve two important outcomes, comfort
and safety, for older drivers who are undergoing normative age-related changes in perceptual,
cognitive, and motor capabilities that affect fitness to drive. We have also reviewed theories of
technology adoption that highlight the importance of perceptions of usability, usefulness (costs
& benefits), and trust, expecting that these frameworks will apply to adoption of ADAS and AV.
A compensation framework (e.g., Charness, Best & Souders, 2012) might be a useful way to
conceptualize the potential reliance tradeoff when developing fully automated AV technology on
our roadways. Do we want to augment age-degraded human abilities with ADAS (addressing the
needs of older drivers contemplating driving cessation), or do we want to substitute robotic
driver technology for human drivers? There are substantial age divides in adoption of other
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modern forms of technology such as computers and the Internet (Charness & Boot, accepted).
Some of the age-related digital divide may be attributed to age-related differences in attitudes
about technology. Given the current uncertainty about older adults’ beliefs and attitudes about
automated vehicles, it is essential to generate population-representative data from these age
cohorts.
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