The Psychology of the Human Factor

Evaluation of an In-Vehicle Driver Support System Among Aging Drivers

LIST OF ABBREVIATIONS

• TDSS Teen Driver Support System
• ODSS Older Driver Support System
• UFOV Useful Field of View

Introduction

In the coming decades, the proportion of the United States population over age 65 will increase significantly (Administration on Aging, 2015).

In 2009, this age group numbered 39.6 million in the United States, is estimated to exceed 55 million by 2020, and is expected to eclipse 72 million by 2030, which will account for roughly one fifth of the driving population in the country (Administration on Aging, 2015). The increase in the total number of older drivers has coincided with an increase in the length of time that they have postponed driving cessation (Sifrit, 2005). Improvements in healthcare and overall life expectancy has resulted drivers staying on the roadway for longer than ever before (Administration on Aging, 2015).

This is important because it has allowed aging drivers to maintain their independence for longer than ever before since the personal automobile is the primary mode of transportation for most Americans.

The ability to maintain independence, specifically through driving, is correlated with greater levels of happiness, independence, autonomy, and overall quality of life (Glasgow & Blakely, 2000). However, the increasing numbers of older adult drivers on our nation’s roads and highways in the coming years will pose several challenges to transportation safety researchers and engineers who focus on safe and efficient vehicular travel.

It has been well documented that significant age-related declines in vision (Owsley & McGwin Jr, 2010), attentional capabilities (Cuenen et al., 2015), cognitive functioning (Aksan et al.

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, 2014), and motor skills (Smither et al., 2004) are highly correlated with an increase in crash-risk. After teenage drivers, older drivers still have the second highest crash-risk for severe and fatal injury crashes (Fars, 2018). Yet, while the number of fatal crashes decreased over the past two decades for teens and middle-aged drivers, they have been steadily increasing for older drivers (NHTSA, 2018). With more older drivers remaining behind the wheel for longer than ever before, this is not entirely unexpected, however, their overall crash rates have failed to decrease at the same rates as some other age groups (NHTSA, 2018).

However, there is still a large portion of drivers over age 65 who do not currently suffer from any such significant age-related declines and have an abundance of driving experience that translates into having relatively low crash rates (McGwin & Brown, 1999). Many older drivers that do experience declines in driving ability choose to adopt strategies in their driving to compensate for these decrements (Glasgow & Blakely, 2000). They do this by decreasing the amount of time spent driving at night, in inclement weather, during rush-hour, and by reducing the number of left-hand turns at busy intersections (Yassuda, Wilson, & von Mering, 1997). Yet, there are many drivers who fail to make appropriate adoptions to their driving habits since they are often unaware of any age-related changes that they may have undergone (Horswill, Sullivan, Lurie-Beck, & Smith, 2012). Aging is highly individual and as such, declines due to general aging are often subtle and gradual, which can result in many drivers being unaware of the severity of specific age-related changes that may impact their ability to safely navigate the roadway (Holland & Rabbitt, 1992).

If drivers are unaware of changes in their behaviors and abilities, they are unlikely to adopt certain compensatory strategies to counteract any age-related declines they have experienced. Rather than relying on individuals to self-moderate, it may be beneficial for them to be receive feedback that allows them to better detect changes in their driving behavior to more accurately assess their current skill level and performance (Broberg & Willstrand, 2014). While multiple assessment methods have been developed to test and predict the abilities of aging drivers (Wood, Horswill, Lacherez, & Antsey, 2012; Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Broberg & Willstrand, 2014), they typically do not provide drivers with specific feedback to their daily driving that may help them to improve their driving and continue to safely maintain their independence. While the ability to accurately assess a driver’s ability to safely navigate the roadway is very useful, assessment methods that also comprise real-world feedback can provide driver’s the ability to adjust their driving to reduce their risk to themselves and other drivers on the roadway.

One way to potentially achieve this is by using in-vehicle, telematic monitoring systems. These systems have begun to become more prevalent in recent years for measuring driver behavior and providing drivers with feedback about their driving behavior and patterns. They monitor, record, and transmit driver behaviors and locations to a central storage location where the data can be summarized and analyzed by different parties. Insurance companies such as Allstate, Progressive, and Geico have developed and introduced their own in-vehicle telematics devices to monitor the behaviors of their drivers to better assess their crash-risk and appropriately adjust their premiums accordingly (“Usage-Based Insurance and Telematics”, 2019).

One limitation of most telematics systems is that if the driver does have access to their data, they can only access their results after each trip, which may limit their ability learn from and correct any risk driving behaviors. Creaser et al. (2015) used an in-vehicle telematics system with teenage drivers that provided them real-time feedback. They found significant improvements in driving behaviors known to be associated with an increased crash-risk for the drivers who received real-time feedback about their driving compared to those who did not receive the real-time feedback. These results were attributed to drivers being able to, in real-time, receive notifications that they were speeding or exhibiting other risky driving behaviors and adapt their driving accordingly.

The current study examined the efficacy of real-time feedback on driving behaviors associated with an increased crash-risk on older adult drivers. Additionally, measures of user experience and satisfaction were taken to identify the likelihood of users adopting such a support system in the future as well as to identify design issues and make recommendations for potential changes to the current system.

This effort will begin with an in-depth review of recent trends in crash data among differing age groups in order to understand how they types of crashes and the circumstances around them change with age. Next will be a review of how declines in hazard perception plays a role in many older adult vehicle crashes. This is followed by a theoretical framework for understanding how attentional selection supports hazard perception and a means for understanding how the effects of aging impact attentional selection in driving, and to a

Next, will be a review of the impact of age-related declines on hazard perception and crash risk among aging drivers. This is followed by a proposed framework for understanding how the role of attention in driving impacts hazard perception and how the effects of aging impact attentional selection in driving. Finally, the role of telematics in driving, specifically among an aging population, will be examined to better understand the potential benefit that can be gained from its implementation.

Review of Driving Safety

 Driving Crash Rates & Trends

It’s a difficult task that requires attention, experience, and safe-driving. We have made cares and roadways safer, but the majority of crashes are due to driver error.

Fatal crashes are primarily a result of speeding, alcohol, seat belt usage, and frailty

Types of contributing factors and how they are changing over time

Motor vehicle collisions are a major worldwide health concern. In 2017, more than 1.35 million people died as a result of a road traffic crash (World Health Organization, 2018). In the United States alone 37,133 drivers died in vehicular crashes 2017, while more than 4.5 million were severely injured enough to require medical treatment (NHTSA, 2018). The economic cost of these road crashes was estimated at more than $413 billion in 2016 alone (National Safety Council, 2018). This estimate includes immediate medical care, long-term medical expenses, property damages, lost productivity, and administrative costs, however, it certainly does not begin to account for the societal effect of the loss of life associated with roadway fatalities. Driver fatalities were the 11th leading cause of death in the United States and were 4th in non-chronic related deaths in 2017 (Centers for Disease Control, 2018).

Encouragingly, the overall number of driving-related fatalities as well as the fatality rate (per 100 million miles travelled) has decreased significantly over the past forty years from 50,331 (3.26 fatalities per 100 million miles) in 1978 to 37,133 (1.16 fatalities per 100 million miles) in 2017 (NHTSA, 2018). However, despite these increases in roadway safety, this is still a high price to pay for personal mobility that both researchers and legislators continually strive to reduce through both education and policy changes regarding driving.

It is getting better, still dangerous, but improving. How is it improving and where is it getting worse?

especially for drivers teenage and older drivers (over age 65), who have the highest crash rates among all drivers (NHTSA, 2018).

Teen Driver Crash Trends

Oldest and youngest drivers are worse, but different in crash types

Teens tend to be risky and are not able to properly scan the roadway for hazards nor can they avoid accidents. They have single-vehicle crashes the most. Experience year-to-year shows that they get safer each year of experience until about 24 or 25 where they level off until about 65

We have focused on improving teens through graduated licensing, more parental monitoring, and feedback and there are fewer teens driving than before.

Teen drivers have long been a cause for concern among due to their level of inexperience behind the wheel and propensity, especially for male drivers, for risk taking (Mayhew, Simpson, & Pak, 2003). Teen drivers are known to intentionally engage in risky driving behaviors associated with increased crash risk (Groeger & Brown, 1989; Laapotti, Keskinen, Hatakka, & Katila, 2001; Rener & Anderle, 1999).

They are also more likely to engage in impaired driving (NHTSA, 2018) and inattentive or distracted driving (Klauer, Guo, Simons-Morton, Ouimet, Lee, & dingus, 2014). Historically, per licensed driver per mile, teen drivers have had the highest fatality rates among all but the very oldest of drivers (Li, Braver, & Chen, 2003; Ferguson, Teoh, & McCartt, 2007). In 2016 there were 2,433 teen driver fatalities (FARS, 2018). The crash rate of drivers ages 16-17 was nearly 4.5 times that of drivers age 30-59 years, with a fatal crash rate of 3.75 per 100 million miles driven (AAA Foundation, 2017).

While these crash rates may seem considerably high, they represent a significant decrease over the past 20 years when the number of teen driver fatalities was 5,439 in 1996 (FARS, 2018). While some of this may be attributed to improvements in the design of vehicles and roadways, much of it may also be due to changes in stricter enforcement of speeding and impaired driving (NHTSA, 2018), driver training programs (McGehee et al., 2007; Simons-Morton et al., 2013), and graduated licensing for teen drivers (Ferguson, Teoh, & McCartt, 2007; Regev, Rolison, & Moutari, 2018).

Older Driver Crash Trends

Trends

Conversely, fatal crash rates for older drivers have seen a steady increase over the past two decades (NHTSA, 2018; FARS 2018). Overall increases in the number of older drivers involved in fatal crashes may be largely attributed to an increase in the overall population of adults over age 65 on the road relative to years past (Cicchino & McCartt, 2015).

However, there has still been a significant increase in the percent change over the past decade in the number of older drivers involved in fatal crashes per licensed driver (NHTSA, 2018). While other age groups have seen decreases or slight increases, the percent of older drivers involved in a fatal crash has risen 29.1% from 2006 to 2017 (NHTSA, 2018; FARS, 2018), with older drivers accounting for 18.2% of all roadway fatalities in 2017 (NHTSA, 2018). These increases in crash rates for older drivers illustrate the need for mediation, however, addressing the contributing factors of crashes older drivers are involved with may not be as straightforward relative to teen drivers (Cicchino & McCartt, 2018).

The number of older adults on the roadway is increasing significantly and will only continue to do so. This is due to several reasons: drivers are living longer, they are staying healthier longer which allows them to continue to drive, the baby boomer generation is inflating the older adult population for the next two decades. While many of these drivers are still completely competent and still have the reasonable visual, motor, and cognitive skills necessary to drive safely, there are still many who are suffering from significant age-related declines. It is important to understand what these declines are and how they may affect driving, not so that we may presume these attributes to be true of all drivers, but that we may understand the risk of the onset of significant age-related declines. This is a slow process that occurs over time that may slowly affect driving, but not at a noticeable rate. Older drivers still tend to rate their driving as being as good as when they were middle-aged, despite evidence to the contrary.

However, when presented with evidence that declines are setting in, they are also open and apt to not only changing their own self-assessment, but also modifying their behaviors. There are many behavioral modifications we see among older adults that are known to have positive effects: decreasing night driving, decreasing left turns, decreasing driving in inclement weather, decreasing driving during rush hour. Yet, despite these adoptions in compensatory driving behaviors that many seniors adopt, this age group still has one of the highest crash risks and fatality rates of all but teen drivers. There are several explanations to this aside from older drivers not being able to drive adequately: they are more fragile and less likely to survive crashes that younger drivers might survive, many of the fatal accidents they are involved with are not their fault (Evans, Traffic Safety book).

The most common types of injury crashes that older drivers are involved in are not related to risky driving behavior, but to lack of attention, proper visual scanning, and poor estimates of the gap and speed of other vehicles. Considering age-related declines are known to significantly impair a driver’s ability to safely navigate the roadway and these declines are known to occur somewhat subtly over time, but older drivers are typically open to modifying their behavior based on evidence of declines, then it is reasonable to conclude that older drivers who are healthy would benefit from some form of support or feedback that allows them to detect when aspects of their driving are beginning to decline.

Older drivers represent the second highest injury and fatality rate per 10,000 licensed drivers, next to teen drivers, and are first in fatalities per 100 million miles driven (NHTSA, 2010). This disproportionate fatality risk is linked to normal, age-related declines in information processing (Parasuraman & Nestor, 1991), visual search abilities (Dickerson et al., 2007), and overall fragility (Langford & Koppel, 2006). Risk is also associated with behavioral factors, such as failure to yield and lower seatbelt use (Koppel, Bohensky, Langford, & Tranto, 2011), and estimation errors, such as misjudging the speed of one’s own vehicle or other vehicles (Hakamies-Blomqvist, 1993). Hakamies-Blomqvist, Mynttinen, Backman, and Mikkonen (1999) measured use of car controls during normal driving for older and middle-aged drivers, and found that unlike middle-aged drivers, older drivers tended to use less than four controls during complex driving scenarios, suggesting a shift to less cognitively complex movements in later ages.

Older drivers are at increased crash risk at intersections. As drivers age, they are more likely to be in a right-angle collision at an intersection crossing (Cooper, 1990) and these crashes make up 55% of older driver multi-vehicle collisions (Hakamies-Blomqvist, 1993). Collisions at intersections are more likely to occur when the older driver is engaging in a left turn rather than a right turn (Keskinene, Ota, & Katila, 1998). Older drivers tend to require more time to decide when a turn is appropriate and tend to require more time to complete a turn (Keskinene et al., 1998). This increased time is likely due to their slower and more frequent fixations in time-limited situations, such as completing a left turn against on-coming traffic, that tend to lead to misjudgments of vehicle speeds and distances (Ho, Scialfa, Caird, & Graw, 2001) as well as psychomotor slowing (Hakamies-Blomqvist, 1994). Older adults have also been found to have very high cognitive load when approaching or driving through an intersection (Keskinene, Ota, & Katila, 1998).

Merging into traffic also tends to be a high crash-risk situation for older drivers. Due to age-related declines in visual search, older drivers have difficulty detecting other vehicles that appear in their peripheral vision and are often surprised by other vehicles when merging into traffic (Kline, Kline, Fozard, Schieber, & Sekuler, 1992).

Environmental factors such as time of day or weather tend to be rarely connected to older driver fatal crashes. Older drivers have fewer nighttime crashes and inclement weather crashes (Hakamies-Blomqvist, 1994). This is likely due to the fact that older adults avoid difficult driving situations, such as nighttime or poor weather condition driving. Furthermore, there has been no difference found between older and younger driver crashes relating to the purpose of trip (Hakamies-Blomqvist, 1994). Older drivers, however, are more likely to be severely injured in dark or unlit areas than younger drivers (Khattack et al., 2002).

Older

Unlike teen drivers, older drivers have a wealth of experience and are far less likely to engage in risky behaviors, and some even have compensatory strategies, some of which are evident in crash data like fewer nighttime fatal crashes, but crash data is not all accurate.

Yet they are getting in to more crashes than every age group except teens.

Older are driving more often, for longer and there are more of them. This is great because it allows them to remain independent which is great

However Older driver Crash rates have been increasing for decades. Part of this is because there are more of them on the roadway and fewer teens, they are statistically going to have more total crashes, yet the increases in crash rates are outpacing the increases in drivers and longevity of maintaining regular driving.

Older drivers tend to have multi-vehicle crashes, typically failing to yield

However, with older drivers, experience and age are not always the best predictors, so license programs based on age are not only unlikely to be effective, but also intrusive and unethical in some regards. Certainly, it may be ok to require check-ins at specific ages tho (Wood paper)

Healthcare cost of older driver crashes and more being on the roadway

Effects of aging. Certainly not all drivers are suffering from significant aging effects, but it is important to understand the effects of different types of aging and how it impacts driving to better understand how to best help drivers mediate their driving to remain on the road as long as possible so as to maintain their independence while not presenting a safety risk to themselves and others

It is accepted that it would not be appropriate to restrict driving eligibility based solely on age for older drivers (Wood, Horswill, Lacherez, & Anstey, 2012). It may be more appropriate for a driver’s eligibility to be based on performance and functional abilities (Owsley & McGwin Jr, 2010).

While there is extensive research regarding age-related functional declines that may be associated with a decrease in driving ability (Aksan, Anderson, Dawson, Uc, & Rizzo, 2014; Shen & Neyens, 2015; Owsley & McGwin Jr, 2010; Shinar,Tractinsky, & Compton, 2004), these results may not generalize to a large portion of the older driver population that is still relatively healthy, both physically and cognitively. However, Cuenen et al. (2015) found that attention capacity was directly correlated to performance among older drivers, yet even when their performance was poor, they still rated themselves as having good performance, suggesting a discrepancy between self-assessment and performance.

These results are relevant when considering the most common types of crashes involving older drivers are due to inattention or failure to yield right of way (NHTSA, 2018). If older drivers are most likely to be involved in a crash due to some form of inattention or poor visual scanning yet are unlikely to be aware of changes in their performance whether due to distraction or general age-related declines (Brober & Willstrand, 2014), it may be relevant to attempt to both reduce inattention while driving and increase self-awareness of driving performance.

So Why are older drivers getting into more crashes if they are slow, risk-averse drivers? Well it would seem that there hazard perception abilities have declined as a result of age-related decrements.

The Aging Driver

Types of crashes and circumstances older drivers are in can often be explained by diminishing hazard perception

Diminishing hazard perception can be explained by some combination of typical age-related declines in areas related to driving, like vision cognition and motor skills

The types of crashes and circumstances around these crashes indicate a lack of hazard perception and avoidance among older drivers. There is a lot of research to support this

While these skills and age-related factors have been shown to impact driving performance, hazard perception is the one skill that has been shown to correlate to crash-risk, thus it is important to understand in greater detail, how hazard perception is impacted with age.

Hazard Perception

This is may be dangerous because…. It can impair our ability to properly detect hazards (Burge). Hazard detection may be impacted by secondary tasks, but also by general inattentiveness and mind wandering.

We need to better understand attention in order to better understand the impact of mind wandering. Older drivers are worse at hazard perception than middle-aged drivers (Horswill, Kemala, Wetton, Scialfa, & Pahcana, 2010). Cite odss study for how the app impacted their attentional abilities positively.

Find research that says whatever their attentional abilities are, especially in familiar environments

Then state how this is imperative since most of their driving, much more so than any other age group, is to and fro the same few areas, as older drivers tend to drive in less familiar environments less so than any other age group.

While there are some drivers with significant impairment, most just experience normal aging, albeit at vastly different and individual rates. Ultimately, the most important things are that drivers remain safe and do not pose a risk to themselves or others on the roadway. Thus, it may be important to consider ways to mediate and improve hazard perception among older drivers. Hazard perception has been argued to be the best predictor of crash-risk and the most meaningful measurement.

Explain what hazard perception is

Explain how attention plays the primary role in hazard perception

While all aging drivers will at some point be affected by the effects of normal age-related declines, many aging drivers may not yet suffer from any such declines and are still completely healthy drivers or have only such mild decrements that they have not yet impacted their driving ability in any noticeable way. Yet, despite so many aging drivers being healthy both physically and mentally, they are still at a higher crash-risk than middle-age drivers.

While there is a myriad of different effects that aging can have on drivers, it is important to not everyone is affected by all these issues. Furthermore, aging is highly individual and the degree and pace at which different faculties decline varies greatly person-to-person.

Attentional Selection

Since hazard perception is primarily related to experience and attention, and older drivers are typically wealthy with driving experience and are relatively risk-free drivers, attention becomes important to examine.

While many older drivers don’t have significant age declines and are experienced, safe-minded drivers, they do still suffer from general decrements of divided attention, and this seems to naturally worsen with age. Errors in selective attention are a major contributing factor in vehicle crashes (Klauer et al., 2104). Because of this, to understand a driver’s ability to safely navigate the roadway accident-free, it is necessary to understand how distraction and attention impact their ability to do so and how these attentional abilities may change as drivers age.

The Four Modes of Attentional Selection, postulated by Trick & Enns (2009), is a more recent model of attention that can be well applied to driving. Their theory proposes a two-dimensional framework for attentional selection while driving based exogenous vs. endogenous processes and automatic vs. controlled processes, which results in four modes of selection:

1. reflexive;
2. habitual;
3. exploratory; and,
4. deliberative

This theory provides a framework for understanding how attention supports driver awareness in differing scenarios and a means for predicting how mind wandering and general inattentiveness will impact the detection of potentially hazardous events on the roadway in terms of disrupting attention allocation.

Discuss 4 modes

Tie in how familiarity and mind wandering then become a big issue with an aging population

 Age-Related Declines

Vision

Low vision is a common contributing factor to older drivers’ high crash risk. Speed and distance become more difficult to judge (Langford & Koppel, 2006). Older adults tend to be slower to recognize traffic, are more likely to misidentify or miss altogether traffic signs (Ho et al., 2001), and their problems reading signs are related to low vision issues (Klein et al., 1992). Failing to detect a posted sign could result in issues ranging from missing a turn or exit to missing a posted reduction in the speed limit. The first may result in a minor hassle for the driver and the latter could result in a catastrophic error leading to crash. Older drivers also appear to avoid driving at night due to deteriorating vision (Langford & Koppel, 2006). An ODSS could help drivers to recognize important signs, such as major roadways and speed changes, through auditory warnings.

An aging population introduces the need for understanding aging performance and limitations in various environments. This review will first present an overview of a few theoretical frameworks to generally understand aging and human performance (Charness, 2008; Salthouse, 1996), describe specific perceptuo-motor and cognitive characteristics of the aging population, and extrapolate these characteristics to possible driving limitations (Smither, Mouloua, Hancock, Duley, Adams, & Latorella, 2004; Mouloua, Smither, Hancock, Duley, Adams, & Latorella, 2004).

Aging Frameworks and Age-Related Effects

Charness (2008) introduces several useful frameworks for understanding general cognitive performance across the aged population, including a processing speed framework (Salthouse, 1996), a neural noise framework (Welford, 1981), and two somewhat related frameworks: brain workload (Cabeza, 2002) and cognitive reserve (Stern, 2009). The processing speed theory (Salthouse, 1996) states that the speed of basic mental processes (e.g., perception, computation, reaction time) are slowed with age.

This slowing means that older individuals will on average perform less information processing per unit of time, especially for unfamiliar tasks. Furthermore, because of this decline in processing speed, the products of this processing are less available for easily making connections or relationships between differing pieces of information, suggesting that managing complexity is a challenge for many older individuals. Welford (1981) postulated, using signal detection theory, that older adults had a lower signal-to -noise ratio in their neural functioning, resulting in slowed performance on perception and reaction tasks, as well as disruption in memory. This leads to downstream effects and design implications for memory performance, where older adults perform better if the environment supports their memory recall with external cues, effectively boosting the signal that would otherwise be lost in neural noise (Craik, 1986).

The other two related frameworks, brain workload and cognitive reserve, reflect the idea that the brain of older adults often must work “harder” to accomplish tasks relative to younger brains with both brain hemispheres contributing to tasks in older brains that were more lateralized in younger brains (Cabeza, 2002), and that older adults vary in the degree of cognitive reserve or resources available to compensate for age-related decline and more severe conditions (Stern, 2009). Both frameworks are detailed and beyond the scope of this review. The primary takeaway for these frameworks is that performing complex tasks are significantly more demanding for older individuals, and that the extent of this increase in mental demand varies significantly between older adults, with those high in cognitive reserve able to buffer the effects of age-related decline for some time.

Smither et al. (2004) and Mouloua et al. (2004) outline specific changes in perceptuo-motor and cognitive capabilities for older adults and their potential challenges for driving. The specific changes for physical functioning are as follows: slower motor response speed, less movement control, less mobility and strength, a decrease in height, slower eye movements, and degraded sensory information in vision and hearing (Smither et al., 2004). The driving related consequences of these motor declines include: less rapid response to driving situations (e.g., using brakes), longer time to initiate and carry out driving maneuvers, less strength to manipulate steering wheel and gauges, changes to the ability to see over the steering wheel and monitor position in traffic, limitations in head mobility to monitor traffic, and slower eye movements to fixate on moving objects (e.g., vehicles).

For visual perception, age-related declines occur for visual acuity, contrast sensitivity, motion-in-depth, gaze stability, critical flicker frequency, and absolute thresholds (Smither et al., 2004). Furthermore, older drivers have changes in color vision and increased sensitivity to glare (Smither et al., 2004). These age-related shifts lead to the following concerns for older drivers: inability to see or easily discriminate highway information such as signs, difficulty seeing with low illumination, poor adjustment to glaring light, difficulty processing color-coded information on the road, impaired gap-judgment and propensity to rear-end vehicles, and impaired tracking of objects.

Fiorentino (2008) analyzed performance on visual and cognitive tests and compared it to performance on a test for monitoring the visual environment, particularly important in driving (i.e., useful field of view, UFOV). The study found that performance on cognitive tests, not visual tests, was associated with performance on the UFOV test for both younger and older drivers, suggesting that cognition is particularly relevant for driving. Mouloua et al. (2004) note the following age-related shifts in cognitive ability: decreased reserve capacity, less working memory capacity, diminished complex judgment, diminished spatial ability, less efficient mental rotation skill, diminished divided attention ability, reduced ability to switch attention between tasks and information sources, limited sustained attention ability, declining selective attention, and slowed processing speed.

These changes lead to many potentially adverse driving implications, including: reduced ability to handle complex driving situations and environments, less ability to recall and remember driving relevant information (e.g., navigation, speed limits), problems in focusing on several pieces of traffic-relevant information at once and maintaining a mental picture of where other vehicles are in the environment, difficulty in translating information from side mirrors, challenges with long stretches of highway, inability to screen out irrelevant information (e.g., which car to pay attention to), and slower to detect and recognize traffic-relevant stimuli in the driving environment (Mouloua et al., 2004).

• Cognition
• Inhibition
• Executive Function
• Working Memory
• Task Complexity
• Significant Cognitive Impairment

Improving Driver Performance

Intro

Start by summarizing everything we know thus far: Older drivers are increasing and driving for longer, yet they are getting into more crashes even tho they are not risky drivers. While we know that significant decrements in age-related decline can impair their driving abilities, many drivers are still relatively healthy and not suffering from any such significant declines other than that of normal aging. However, this does include changes in attentional abilities and processes. Additionally, as they age they may be unaware of their abilities worsening. A possible solution is to begin to change driver’s attitudes and assessments of themselves as a driver so that they may be able to adjust their driving to reduce their crash-risk and not only make the road safer for themselves and other drivers, but also increase their ability to remain on the roadway longer, thus safely maintaining their independence.

What we see is that as long as older drivers are not suffering from significant cognitive declines associated with aging, and are attentively engaged with the environment, they tend to be quite safe drivers. So a technology or the ability to keep them engaged attentively, but also provide feedback so that they can properly assess their own behaviors and skill levels and make necessary changes to their habits or behaviors, gives them the best chance at reducing their crash-risk and staying on the road.

Then it segues to driver attitudes and openness to correction & change

Driver Attitudes

With teens, we see evidence that experience is the biggest predictor of crash-risk. GDL’s have been shown to be effective in 16 & 17 year olds, but teens with and without GDL’s have the same crash-rate at 18 once they are fully on their own. Many argue this demonstrates the ineffectiveness of GDL’s, however, the opposite seems to be true. While it may provide evidence that there aren’t long-term effects of being in a GDL, it does demonstrate that restricting drivers to situations that they are more comfortable and minimizing dangerous situations is effective. The same goes for older drivers with compensatory strategies like avoiding night driving.

Older drivers may be open to acknowledging their driving decrements and adjusting AND assessments of older drivers may be useful, but the problem is that many older drivers value their privacy and freedom and independence and do not always like the idea of involving others in this delicate matter which may make them more willing to privately be assessed and adopt driving changes.

When we talk about older drivers and compensatory strategies, it is often regarding drivers with severe age-related declines. What about those that are only experience mild declines? They are less likely to notice the subtle changes and make adjustments, yet could still benefit from feedback and training!

To do this, we need to know the specifics of their common crash types, the impact of significant aging decrements, frailty effects, how likely are they to moderate their behaviors, what moderating behaviors work the best, what’s the best way to get them to acknowledge they may need to moderate their behavior, and how effective is training

Are there potential methods that have shown to be effective? Training, telematics, assessments making them more aware of their driving skill

We can recommend compensatory strategies for drivers with significant vision, cognitive, motor, and attention loss, but what about “healthy” adults who are experiencing the effects of aging, but not yet to the extent that it is considered significant?

Well they too are still getting into accidents. 1. We need to improve driving awareness and decrease mind wandering. Several papers discuss how bad this is and Trick framework supports this when thought in conjunction with the fact that older drivers tend to mostly driver in familiar environments where lackadaisical driving is bred along with a higher propensity for mind wandering. 2. We need to make them aware of their driving behaviors so that they can more accurately assess their own driving skill and identify specific areas that they may need to adjust how they driver, like speeding or braking.

Overall, more awareness of driving should lead to better self-assessment, adjustments, and less mind wandering. All things that are critical for older drivers to avoid accidents whether they are at fault or the other driver. This will allow them to stay on the roadway longer as many older drivers will often cease driving after a severe crash. Let’s help them to maintain the ability to avoid those situations safely for their sake and those around them so that they can maintain their independence and quality of life.

Families and practitioners generally prefer to avoid this uncomfortable topic of driving cessation

They overrate their skills, but when presented with contrary, objective evidence, they are open to changing their habits to stay on the roadway

So, they need to be presented with evidence that allows them to monitor and update their driving behaviors because they are bad at self-assessing (Horswill, Antsey, Hatherly, Wood, & Pachana; 2011).

Older Driver Assessments

Lots of assessments that seem to be great, but the problem is these often lack the ability to implement solutions for training of better driving. Cite all the assessments for older driver crash-prediction. They may however help to identify drivers who need help with training.

Role of Training in Driving

General Lessons of Training Efficacy

In general, research on training says we can make people better through feedback

Kirk, Salas, Keebler, and notes from Tony screenshots.

We know that deliberate practice as a result of feedback is one of the best ways to improve performance (Erkicsson & Lehman)

Effects of Training on Aging Driver’s Performance

Literature on aging drivers suggest we can improve their awareness and abilities as well as propensity to adopt compensatory strategies. One way to combat this may also be through giving them feedback about their actual driving performance and habits via telematics

Shinar (2005) demonstrates that drivers can improve their performance with practice, but that older drivers are the worst at it. This indicates that older drivers may benefit from more repeated feedback regarding driving rather than intermittent assessments. This is the transition into telematics and feedback

Horswill, Kemala, Wetton, Scialfa, & Pachana (2010) were able to train older drivers to be better at hazard perception

The system was previously used and evaluated among teen drivers and found to be effective in mediating driving behavior as well as increasing the accuracy of self-assessments of performance (Brovold et al., 2007; Creaser el al., 2011; Creaser et al., 2015).

Use of Telematics in Driving

Telematic Systems

What are telematics?

Current Uses

Insurance companies, fleet services, and some driver training programs, mostly aimed at teens

Teen Driver Support Systems

Mention existence of other teen support systems that allow parents to monitor and provide feedback to kids and how this has seemingly helped their crash rates decrease

Then discuss TDSS

• Measures foreground vs. background usage of app
• Miles traveled & number of trips
• 6 Speed bins
• Hard Braking
• Aggressive Acceleration
• Excessive Turning
• Stop Signs
• Speed change and curve warnings

The present study tests whether the function of a Teen Driver Support System (TDSS) generalizes to an older population with unique limitations and capabilities, as an Older Driver Support System (ODSS). The TDSS provided real-time and continuous monitoring of known driving risk variables, such as speeding and vehicle maneuvers (Creaser et al., 2015). If the Older Driver Support System (ODSS) proves effective and usable for older drivers, it could prove to be a significant technological aid for road safety while helping older drivers maintain driving independence and mobility in their later years.

· The research study uses a mixed methods approach (Creswell & Clark, 2007; Creswell, 2013) building on previous work and information collected through the HumanFIRST laboratory’s previous studies (i.e., Safe Teen Car and the Teen Driver Support System) through sequential activities to collect both qualitative and quantitative data to enhance and modify the TDSS into an ODSS. The two purposes of this approach were to first, complement the work of the previous TDSS (Creaser et al., 2014) and Safe Teen Car (Manser, et. al, 2013) projects, and second, to develop the technology through a deliberate research structure by which each step informs the next (Greene, Caracelli, & Graham, 1989).

Older Driver Support Systems

A driver support system that not only monitors and records driver behaviors, but also one that provides the driver with unique, real-time feedback about their driving and the roadway may thus be beneficial in not only giving drivers a more accurate assessment of how they are driving, particularly in ways that are associated with increased crash risk, but also in keeping the drivers mind on the task of driving and potentially minimizing mind wandering while driving.

Talk about results of ODSS- high satisfaction, low mind wandering, low visual distraction

Such a system may also aid in navigation and way-finding on the road. The quality of decisions made by older drivers is equivalent to those of younger drivers, but the speed at which those decisions are made vary as a function of age, with older drivers taking longer (Walker, Fain, Fisk, & McGuire, 1997). On the road, decision-making time may be limited, and a system such as the ODSS, while not specifically a way-finding system, may significantly reduce monitoring and scanning time for speed and speeding related information, freeing up mental resources for a decision-making task related to navigation. A related note is that older drivers are more prone to fatigue and may get tired on long journeys (Langford & Koppel, 2006), and a system like the ODSS that takes over some of the mental requirements of the driving task may be beneficial for longer or unfamiliar routes that older drivers find challenging (Burns, 1999).

Given both general behavioral research and concrete driving research have demonstrated problematic issues with older drivers on the road, addressing these issues has become a focus of technological intervention (Ball 2006). Ball (2006) notes that technology can assist the needs of aging drivers through improvements in driving assessment (e.g. UFOV test) and rehabilitation (i.e., vision, education, and cognition). The ODSS reflects both such advancements for in-vehicle technologies and highway enhancements as it utilizes vehicle location information and mapping services to provide both immediate and predictive information to the drivers for support.

Finally, the use of technologies to provide driving feedback have been shown to be particularly helpful for older adults, suggesting that the ODSS has the potential to positively affect driving behavior outside of the immediate driving situation (Ackerman, Crowe, Vance, Wadley, Owsley, & Ball, 2011). Older drivers were scored on the UFOV test and given feedback on whether their scores qualified them for an insurance discount. Their feedback was found to be related to their avoidance of difficult driving conditions, with those not receiving the discount due to poor UFOV scores being less likely to drive in more dangerous driving scenarios (Ackerman et al., 2011). This suggests that the ODSS can provide relevant feedback about questionable driving behavior, and older drivers can use this information to help inform appropriate driving strategies and moderate their involvement in dangerous driving environments.

 Purpose of the Current Study

The current study is a continuation of several previous experiments completed by the HumanFIRST laboratory that utilized a smartphone-based driver support system to increase awareness of driving habits and decreases the propensity for risky driving behaviors. Early studies with teen drivers (Creaser et al., 2011; Manser et al., 2013; Creaser, et al., 2015) found that teen drivers who received real-time feedback were significantly less likely to exhibit driving behaviors associated with increased crash risk (e.g., speeding, excessive acceleration, hard braking, running stop signs, etc.). The next step in this line of research was to develop a more user-friendly version of the driver support app for use with drivers of all ages (henceforth referred to as RoadCoach), which included several focus groups and simulator-based