Older adults in the United States rely heavily on driving their own vehicles to commute to work, shop for groceries, and access public services. To effectively help older adults maintain mobility and independence,we need to better understand how thecognitive, visual functioning, and health declines influence their tendency to self-restrict their driving. The objective of this study is to develop a causal model to examine the effects of age, gender, household status (specifically living alone), physical, cognitive, visual abilities, and health status on older adults' driving mobility in terms of driving exposure and avoidance.
View Article and Find Full Text PDFBackground And Objectives: Older drivers are overrepresented in collisions at intersections while making left turns across oncoming traffic. Using naturalistic driving methods, we evaluated the association between vision impairment and their left-turn characteristics.
Research Design And Methods: In this prospective, observational study, vision impairment as defined by visual acuity, contrast sensitivity, visual processing speed, visual field sensitivity, and motion perception was assessed in drivers ≥70 years old.
Background: This study aimed to evaluate the association between a Certified Driving Rehabilitation Specialist's (CDRS) ratings of on-road driving performance by older drivers and at-fault crash and near-crash involvement using naturalistic driving techniques where crashes and near-crashes are recorded in everyday driving through in-vehicle instrumentation.
Methods: This is a cohort study of 144 drivers aged 70 years and over who were recruited due to a recent ophthalmology clinic visit at the University of Alabama at Birmingham. Baseline measurements consisted of demographics, visual status, and other health variables.
Importance: Government motor vehicle crash reports used in the study of driver safety can be biased and incomplete. Naturalistic driving methods using in-vehicle instrumentation have been developed in recent years to objectively measure crashes and near crashes as they occur on the road using video and vehicle kinematic data.
Objective: To examine visual risk factors associated with at-fault crashes and near crashes among older drivers, most of whom have age-related eye conditions associated with vision impairment.
Introduction: Lane changes can be a complicated maneuver occurring a dynamic environment requiring the integration of many streams of information. Older drivers may struggle with lane changes which may elevate crash risk.
Methods: Real-world lane change behaviors were examined using the Second Strategic Highway Research Program Naturalistic Driving Study database.
Background And Objectives: The increasing number of senior drivers may introduce new road risks due to age-related declines in physical and cognitive abilities. Advanced driver assistance systems (ADAS) have been proposed as solutions to minimize age-related declines, thereby increasing both senior safety and mobility. This study examined factors that influence seniors' attitudes toward adopting ADAS after significant exposure to the technology in naturalistic settings.
View Article and Find Full Text PDFBackground: Older drivers aged ≥70 years old have among the highest rates of motor vehicle collisions (MVC) compared to other age groups. Driving is a highly visual task, and older adults have a high prevalence of vision impairment compared to other ages. Most studies addressing visual risk factors for MVCs by older drivers utilize vehicle accident reports as the primary outcome, an approach with several methodological limitations.
View Article and Find Full Text PDFIntroduction: This paper evaluated the low mileage bias (LMB) phenomenon for senior drivers using data mined from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. Supporters of the LMB construct postulate that it is only those seniors who drive the lowest annual mileage who are primarily responsible for the increased crash rates traditionally attributed to this population in general.
Method: The current analysis included 802 participants, all aged 65 or older who were involved in 163 property damage and injury crashes, and deemed to be at-fault in 123 (75%) of those instances.
Understanding causal factors for traffic safety-critical events (e.g., crashes and near-crashes) is an important step in reducing their frequency and severity.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
March 2016
The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only.
View Article and Find Full Text PDFBackground: Driver distraction is a major contributing factor to crashes, which are the leading cause of death for the US population under 35 years of age. The prevalence of secondary-task engagement and its impacts on distraction and crashes may vary substantially by driver age.
Methods: Driving performance and behaviour data were collected continuously using multiple cameras and sensors in situ for 3542 participant drivers recruited for up to 3 years for the Second Strategic Highway Research Program Naturalistic Driving Study.
J Safety Res
September 2015
Problem: As our driving population continues to age, it is becoming increasingly important to find a small set of easily administered fitness metrics that can meaningfully and reliably identify at-risk seniors requiring more in-depth evaluation of their driving skills and weaknesses.
Method: Sixty driver assessment metrics related to fitness-to-drive were examined for 20 seniors who were followed for a year using the naturalistic driving paradigm. Principal component analysis and negative binomial regression modeling approaches were used to develop parsimonious models relating the most highly predictive of the driver assessment metrics to the safety-related outcomes observed in the naturalistic driving data.
The purpose of this research effort was to compare older driver and non-driver functional impairment profiles across some 60 assessment metrics in an initial effort to contribute to the development of fitness-to-drive assessment models. Of the metrics evaluated, 21 showed statistically significant differences, almost all favoring the drivers. Also, it was shown that a logistic regression model comprised of five of the assessment scores could completely and accurately separate the two groups.
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