Changes in driving acceleration pattern variability related to cognitive and physical health

Howcroft, Jennifer; Wallace, Bruce; Goubran, Rafik; Porter, Michelle M.; Knoefel, Frank

doi:10.1109/bhi.2018.8333442

Cited by 4 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They found a number of nuanced patterns that demonstrated that variability in driving behavior may be a result of a combination of health and the requirements of the driving task. For example, they showed that variability in deceleration decreased with poorer cognition (Howcroft et al, 2019), but increased in some instances with poorer physical health; meanwhile variability in acceleration decreased with poorer physical health, and increased with a combination of poorer physical and cognitive health (Howcroft et al, 2018). The authors surmise that there may be situations in which drivers with poorer health are unable to adapt to changes in the driving environment (reducing variability), and others where variability is unnecessarily increased.…”

Section: Discussionmentioning

confidence: 99%

The Validity of Three New Driving Simulator Scenarios: Detecting Differences in Driving Performance by Difficulty and Driver Gender and Age

et al. 2020

Self Cite

View full text Add to dashboard Cite

Objective We explored the convergent and discriminant validity of three driving simulation scenarios by comparing behaviors across gender and age groups, considering what we know about on-road driving. Background Driving simulators offer a number of benefits, yet their use in real-world driver assessment is rare. More evidence is needed to support their use. Method A total of 104 participants completed a series of increasingly difficult driving simulation scenarios. Linear mixed models were estimated to determine if behaviors changed with increasing difficulty and whether outcomes varied by age and gender, thereby demonstrating convergent and discriminant validity, respectively. Results Drivers adapted velocity, steering, acceleration, and gap acceptance according to difficulty, and the degree of adaptation differed by gender and age for some outcomes. For example, in a construction zone scenario, drivers reduced their mean velocities as congestion increased; males drove an average of 2.30 km/hr faster than females, and older participants drove more slowly than young (5.26 km/hr) and middle-aged drivers (6.59 km/hr). There was also an interaction between age and difficulty; older drivers did not reduce their velocities with increased difficulty. Conclusion This study provides further support for the ability of driving simulators to elicit behaviors similar to those seen in on-road driving and to differentiate between groups, suggesting that simulators could serve a supportive role in fitness-to-drive evaluations. Application Simulators have the potential to support driver assessment. However, this depends on the development of valid scenarios to benchmark safe driving behavior, and thereby identify deviations from safe driving behavior. The information gained through simulation may supplement other forms of assessment and possibly eliminate the need for on-road testing in some situations.

show abstract

Section: Discussionmentioning

confidence: 99%

The Validity of Three New Driving Simulator Scenarios: Detecting Differences in Driving Performance by Difficulty and Driver Gender and Age

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, a baseline of their driving ability and typical behaviors can be formed. Ongoing and future work needs to focus on the identification of driver behaviors that are indications of possible health changes [29]. Monitoring driving over longer periods would allow the identification of changes in driving ability for a given driver.…”

Section: Discussionmentioning

confidence: 99%

Naturalistic Driving: A Framework and Advances in Using Big Data

2018

Self Cite

View full text Add to dashboard Cite

Abstract:Driving is an activity that facilitates physical, cognitive, and social stimulation in older adults, ultimately leading to better physical and cognitive health. However, aging is associated with declines in vision, physical health, and cognitive health, all of which can affect driving ability. One way of assessing driving ability is with the use of sensors in the older adult's own vehicle. This paper provides a framework for driving assessment and addresses how naturalistic driving studies can assist in such assessments. The framework includes driving characteristics (how much driving, speed, position, type of road), actions and reactions (lane changes, intersections, passing, merging, traffic lights, pedestrians, other vehicles), destinations (variety and distance, sequencing and route planning), and driving conditions (time of day and season). Data from a subset of Ottawa drivers from the Candrive study is used to illustrate the use of naturalistic driving data. Challenges in using naturalistic driving big data and the changing technology in vehicles are discussed.

show abstract

Driving Destination Measures in Older Adult Drivers with Differing Health Statuses

Howcroft¹,

Wallace²,

Goubran³

et al. 2018

2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

View full text Add to dashboard Cite

Changes in driving acceleration pattern variability related to cognitive and physical health

Cited by 4 publications

References 17 publications

The Validity of Three New Driving Simulator Scenarios: Detecting Differences in Driving Performance by Difficulty and Driver Gender and Age

The Validity of Three New Driving Simulator Scenarios: Detecting Differences in Driving Performance by Difficulty and Driver Gender and Age

Naturalistic Driving: A Framework and Advances in Using Big Data

Driving Destination Measures in Older Adult Drivers with Differing Health Statuses

Contact Info

Product

Resources

About