Semi-Autonomous Vehicles as a Cognitive Assistive Device for Older Adults

Applications of Self-Driving Vehicles in an Aging Population

Unlabelled

The proportion of older adult drivers is increasing and represents a growing population that must contemplate reducing driving and eventually stopping driving. The advent of self-driving vehicles opens vast possibilities with practical and far-reaching applications for our aging population. Advancing technologies in transportation may help to overcome transportation barriers for less mobile individuals, transcend social and geographical isolation, and improve resource and medical access. Herein, we propose various applications and benefits that self-driving vehicles have in maintaining independence and autonomy specifically for our aging population to preserve aging.

Special users with special needs in autonomous vehicles: A systematic review

BACKGROUND

Automated driving offers enjoyable and comfortable trips, though not everyone will find the experience pleasant. Some special populations such as elderly people, individuals with physical or cognitive impairments, and pregnant women face different difficulties in mobility services to conduct their daily activities and, consequently, also in using new technologies like autonomous vehicles.

OBJECTIVE

This study aims to review the specific needs, concerns, and difficulties of special populations when using autonomous vehicles and to highlight the primary issues affecting their acceptance of these technologies.

METHODS

A review of 48 papers was conducted to identify the special needs, concerns, and difficulties faced by various demographic groups, including elderly individuals, people with physical disabilities, and pregnant women, in using or approaching the use of autonomous vehicles.

RESULTS

The review revealed that aging individuals have received more attention by scientific community compared to other disabled populations. Acceptance of autonomous vehicles was identified as the primary issue across all surveyed groups. Safety, comfort, and dependability were also significant concerns, particularly among individuals with special needs.

CONCLUSIONS

This study outlines the unique concerns of various demographic groups using autonomous vehicles, with acceptance highlighted as a common issue. Designing user-friendly interfaces tailored to diverse demographics could enhance acceptance and improve the overall travel experience for those needing constant support.

Automated vehicles for older adults with cognitive impairment: a survey study

As the population is ageing, the number of older adults with cognitive impairment (CI) is increasing. Automated vehicles (AVs) can improve independence and enhance the mobility of these individuals. This study aimed to: (1) understand the perception of older adults (with and without CI) and stakeholders providing services and supports regarding care and transportation about AVs, and (2) suggest potential solutions to improve the perception of AVs for older adults with mild or moderate CI. A survey was conducted with 435 older adults with and without CI and 188 stakeholders (e.g. caregivers). The results were analysed using partial least square - structural equation modelling and multiple correspondence analysis. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided recommendations to improve older adults' perception of AVs including education and adaptive driving simulation-based training.Practitioner summary: This study investigated the perception of older adults and other stakeholders regarding AVs. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided guidelines to improve older adults' perception of AVs.

Driving Automation for Older Adults with Preclinical Alzheimer's Disease

INTRODUCTION

Older adults with preclinical Alzheimer's disease (AD) show changes in on-road driving performance. The impact of preclinical AD on using automated vehicle (AV) technology is unknown. The aim was to evaluate safety and cognitive workload while operating AV technology in drivers with preclinical AD.

INTRODUCTION

This cross-sectional study included 40 participants: 19 older adults (age 74.16 ± 4.78; MOCA scores 26.42 ± 2.52) with preclinical AD, evidenced by elevated cortical beta-amyloid; and 21 controls (age 73.81 ± 5.62; MOCA scores 28.24 ± 1.67). All participants completed two scenarios in a driving simulator. Scenario 1 included conditional automation with an emergency event that required a manual take-over maneuver. Scenario 2 was identical but with a cognitive distractor task. Emergency response time was the main safety outcome measure. Cognitive workload was calculated using moment-to-moment changes in pupillary size and converted into an Index of Cognitive Activity (ICA). Mann-Whitney U and independent t tests were used to compare group differences.

RESULTS

Emergency response times were similar between drivers with preclinical AD and controls in scenario 1 (20.85 s ± 1.08 vs. 20.52 s ± 3.18; p = 0.83) and scenario 2 (14.83 s ± 7.37 vs. 13.45 s ± 10.43; p = 0.92). Likewise, no differences were found in ICA between drivers with preclinical AD and controls in scenario 1 (0.34 ± 0.08 vs. 0.33 ± 0.17; p = 0.74) or scenario 2 (0.30 ± 0.07 vs. 0.29 ± 0.17; p = 0.93).

CONCLUSIONS

Older drivers with preclinical AD may safely operate AV technology, without increased response times or cognitive workload. Future on-road studies with AV technology should confirm these preliminary results in drivers with preclinical AD.

Automated Vehicles for People With Dementia: A "Tremendous Potential" That "Has Ways to go"-Reports of a Qualitative Study

BACKGROUND AND OBJECTIVES

The prospect of automated vehicles (AVs) has generated excitement among the public and the research community about their potential to sustain the safe driving of people with dementia. However, no study to date has assessed the views of people with dementia on whether AVs may address their driving challenges.

RESEARCH DESIGN AND METHODS

This mixed-methods study included two phases, completed by nine people with dementia. Phase I included questionnaires and individual semistructured interviews on attitudes toward using different types of AVs (i.e., partially or fully automated). Interpretative phenomenological analysis was used to assess participants' underlying reasons for and against AV use. The participants' identified reasons against AV use informed the focus group discussions in Phase II, where participants were asked to reflect on potential means of overcoming their hesitancies regarding AV use.

RESULTS

The results showed that people with dementia might place higher levels of trust in fully automated compared to partially automated AVs. In addition, while people with dementia expressed multiple incentives to use AVs (e.g., regaining personal freedom), they also had hesitations about AV use. These hesitancies were based on their perceptions about AVs (e.g., cost), their own abilities (i.e., potential challenges operating an AV), and driving conditions (i.e., risk of driving in adverse weather conditions).

DISCUSSION AND IMPLICATIONS

The findings of this study can help promote the research community's appreciation and understanding of the significant potential of AVs for people with dementia while elucidating the potential barriers of AV use by people with dementia.

Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition

Shared autonomous vehicle services (i. e., automated shuttles, AS) are being deployed globally and may improve older adults (>65 years old) mobility, independence, and participation in the community. However, AS must be user friendly and provide safety benefits if older drivers are to accept and adopt this technology. Current potential barriers to their acceptance of AS include a lack of trust in the systems and hesitation to adopt emerging technology. Technology readiness, perceived ease of use, perceived barriers, and intention to use the technology, are particularly important constructs to consider in older adults' acceptance and adoption practices of AS. Likewise, person factors, i.e., age, life space mobility, driving habits, and cognition predict driving safety among older drivers. However, we are not sure if and how these factors may also predict older adults' intention to use the AS. In the current study, we examined responses from 104 older drivers (M _age = 74.3, SD _age = 5.9) who completed the Automated Vehicle User Perception Survey (AVUPS) before and after riding in an on-road automated shuttle (EasyMile EZ10). The study participants also provided information through the Technology Readiness Index, Technology Acceptance Measure, Life Space Questionnaire, Driving Habits Questionnaire, Trail-making Test Part A and Part B (TMT A and TMT B). Older drivers' age, cognitive scores (i.e., TMT B), driving habits (i.e., crashes and/or citations, exposure, and difficulty of driving) and life space (i.e., how far older adults venture from their primary dwelling) were entered into four models to predict their acceptance of AVs-operationalized according to the subscales (i.e., intention to use, perceived barriers, and well-being) and the total acceptance score of the AVUPS. Next, a partial least squares structural equation model (PLS-SEM) elucidated the relationships between, technology readiness, perceived ease of use, barriers to AV acceptance, life space, crashes and/or citations, driving exposure, driving difficulty, cognition, and intention to use AS. The regression models indicated that neither age nor cognition (TMT B) significantly predicted older drivers' perceptions of AVs; but their self-reported driving difficulty (p = 0.019) predicted their intention to use AVs: R ² = 6.18%, F (2,101) = 4.554, p = 0.040. Therefore, intention to use was the dependent variable in the subsequent PLS-SEM. Findings from the PLS-SEM (R ² = 0.467) indicated the only statistically significant predictors of intention to use were technology readiness (β = 0.247, CI = 0.087-0.411) and barriers to AV acceptance (β = -0.504, CI = 0.285-0.692). These novel findings provide evidence suggesting that technology readiness and barriers must be better understood if older drivers are to accept and adopt AS.

Can Automated Vehicles Be Useful to Persons Living With Dementia? The Perspectives of Care Partners of People Living With Dementia

Background and Objectives

Driving cessation is a complex challenge with significant emotional and health implications for people with dementia, which also affects their family care partners. Automated vehicles (AVs) could potentially be used to delay driving cessation and its adverse consequences for people with dementia and their care partners. Yet, no study to date has investigated whether care partners consider AVs to be potentially useful for people with dementia.

Research Design and Methods

This mixed-methods study assessed the views of 20 former or current family care partners of people with dementia on AV use by people with dementia. Specifically, questionnaires and semistructured interviews were used to examine care partners’ acceptance of AV use by people with dementia and their views about the potential usefulness of AVs for people with dementia.

Results

The results demonstrated that care partners identified possible benefits of AV use by people with dementia such as their anticipated higher social participation. However, care partners also voiced major concerns around AV use by people with dementia and reported significantly lower levels of trust in and perceived safety of AVs if used by the person with dementia in their care compared to themselves. Care partners’ concerns about AV use by people with dementia included concerns around the driving of people with dementia that AVs are not designed to address; concerns that are specific to AVs but are not relevant to the nonautomated driving of people with dementia; and concerns that arise from existing challenges around the nonautomated driving of people with dementia but may be exacerbated by AV use.

Discussion and Implications

Findings from this study can inform future designs of AVs that are more accessible and useful for people with dementia.

Occupational Therapy Interventions to Improve Driving Performance in Older People With Mild Cognitive Impairment or Early-Stage Dementia: A Systematic Review

IMPORTANCE

For a person with mild cognitive impairment (MCI) or early-stage dementia, driving is important for independence. However, driving presents safety concerns for both the person and family members. It is important to determine whether occupational therapy interventions can prolong safe driving for this population.

OBJECTIVE

To determine the effectiveness of occupational therapy interventions to improve driving performance in older people with MCI or early-stage dementia.

DATA SOURCES

We conducted a search of MEDLINE, PsycINFO, CINAHL, and gray literature using Google Scholar. Study Selection and Data Collection: Studies were included if they evaluated interventions that (1) aimed to improve the driving performance of older people (M age ≥60 yr) with MCI or early-stage dementia and (2) could be designed or delivered by an occupational therapy practitioner who specializes in driving. Citations were reviewed independently by two authors, and quality appraisal was conducted using the Cochrane risk-of-bias guidelines.

FINDINGS

One Level I randomized controlled trial (RCT) and 4 Level III quasi-experimental studies were included; these studies had 231 participants in total with reported M ages of 65.6-72.5 yr. One study evaluated a compensatory approach, whereas the others evaluated a remedial approach. The studies used different measures to assess outcomes and reported mixed effects.

CONCLUSIONS AND RELEVANCE

Low strength of evidence suggests that occupational therapy interventions may improve the driving performance of older people with MCI or early-stage dementia. More RCTs are needed that include long-term follow-up measures and address clinically important outcomes. What This Article Adds: In the absence of conclusive evidence from research studies and best practice guidelines, occupational therapy practitioners must rely on their clinical experience and their clients' abilities. Development of evidence and guidelines in this area is critical. It is also important for practitioners to work closely with clients, families, and interdisciplinary team members to carefully monitor fitness to drive.

Attitudes Toward Four Levels of Self-Driving Technology Among Elderly Drivers

Automatization and autonomous vehicles can drastically improve elderly drivers' safety and mobility, with lower costs to the driver and the environment. While autonomous vehicle technology is developing rapidly, much less attention and resources have been devoted to understanding the acceptance, attitudes, and preferences of vehicle automatization among driver groups, such as the elderly. In this study, 236 elderly drivers (≥65 years) evaluated four vehicles representing SAE levels 2–5 in terms of safety, trustworthiness, enjoyment, reliability, comfort, ease of use, and attractiveness, as well as reporting preferences for vehicles employing each of the four levels of automation. The results of a repeated-measures ANOVA showed that the elderly drivers rated the SAE level 2 vehicle highest and the fully automated vehicle (SAE 5) lowest across all attributes. The preference for the vehicle declined as a function of increasing automatization. The seven attributes formed an internally coherent “attitude to automatization” scale, a strong correlate of vehicle preference. Age or annual mileage were not related to attitudes or preferences for automated vehicles. The current study shows that elderly drivers' attitudes toward automatization should be studied further, and these results should be taken into account when developing automated vehicles. The full potential of automatization may not be realized if elderly drivers are ignored.

Older Adult Willingness to Use Fully Autonomous Vehicle (FAV) Ride Sharing

In the United States, older adults (age 65 and older) rely on private automobiles for transportation. For those who stop driving, access to alternative modes of transportation is important for health, wellbeing, mobility, and independence. This paper explores older adult willingness to use fully autonomous vehicle (FAV) ride sharing and the features or services of FAV ride sharing that would make them willing to take a ride. These data were gathered as part of a larger qualitative research study designed to explore the factors affecting older adult use of ride share services. For the larger study, we conducted 68 telephone interviews with older adults, and 10 in-person focus groups with 56 older adults, including individuals who both used and never used ride share services. We used a convenience sample recruited by study partners, including ride share and transportation services and a recruitment firm. The predominant thematic findings of the qualitative analysis included a desire for a proven safety record in terms of performance and technology, followed by dependability and accuracy of FAV ride sharing. Older adults' concerns about FAV ride sharing included safety concerns and preferences for social interaction with drivers. Ride share services that use FAVs in the future may need to tailor transportation offerings for older adults to increase their willingness to use FAVS to support their mobility and social needs.

Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study

Background

With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data.

Objective

The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident.

Methods

At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data—neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)—from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models.

Results

We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data.

Conclusions

Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.