Improvement Direction of Conditional Driving License System for the Elderly Drivers

2020 ◽  
Vol 19 (5) ◽  
pp. 29-39
Author(s):  
Sangjin Han ◽  
◽  
Hyoseuk Chang ◽  
Junhan Cho ◽  
Juseok Oh ◽  
...  
Keyword(s):  
The Elderly ◽  
Driving License ◽  
Elderly Drivers ◽  
License System

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Timo Lajunen ◽  
Mark J. M. Sullman
Keyword(s):  
Repeated Measures ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
The Elderly ◽  
Ease Of Use ◽  
Full Potential ◽  
Automated Vehicles ◽  
Elderly Drivers ◽  
Strong Correlate ◽  
Four Levels

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.

scholarly journals The effect of driving simulator program on elderly drivers

2020 ◽  
Author(s):  
Deokju Kim
Keyword(s):  
Cognitive Function ◽  
Visual Perception ◽  
Frontal Lobe ◽  
Driving Simulator ◽  
The Elderly ◽  
Safe Driving ◽  
Elderly Drivers ◽  
Positive Effects ◽  
Frontal Lobe Function ◽  
Simulator Program

Abstract Background: The purpose of the present study was to present evidence for driving interventions for the elderly by conducting programs that can improve visual perception and cognitive function in a driving simulator for elderly drivers and analyse their effects.Methods: Three elderly subjects who were 65 years or older, did not have physical and cognitive impairments, and were able to drive themselves participated in the present study. A total of 12 intervention sessions, of which subjects participated in 10 sessions of nine different visual perception and cognitive function programs available in a driving simulator, were conducted and pre- and post-program assessments were conducted (two assessments in total). The assessments included the evaluation of visual perception, frontal lobe function, concentration, safe driving behaviour, and self-efficacy.Results: The results showed positive effects of the driving simulator program on the visual perception, frontal lobe function, concentration, safe driving behaviour, and self-efficacy of all subjects. Changes in the simulator results showed remarkable improvement in the response evaluation, judgment evaluation, and predictive power evaluation, but showed difficulties in interference tasks and depth perception in common.Conclusions: The results showed positive effects of driving simulator training on the driving ability of the elderly, and consistent provision of such training is expected to improve the quality of life of the elderly by securing the safety of driving and actively supporting social participation.

scholarly journals Age-Related Diseases and Driving Safety

Geriatrics ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 80
Author(s):  
Michael Falkenstein ◽  
Melanie Karthaus ◽  
Ute Brüne-Cohrs
Keyword(s):  
Diabetes Mellitus ◽  
Driving Simulator ◽  
The Elderly ◽  
Cognitive Disorder ◽  
Driving Safety ◽  
Elderly Drivers ◽  
Age Related ◽  
Independent Life ◽  
Driving Fitness ◽  
The Impact

Due to demographic changes, the number of older drivers is steadily increasing. Mobility is highly relevant for leading an independent life in the elderly. It largely depends on car driving, which is a complex task requiring a multitude of cognitive and motor skills vulnerable to age- related functional deterioration. The almost inevitable effects of senescence may be potentiated by age-related diseases, such as stroke or diabetes mellitus. Respective pharmacological treatment may cause side effects, additionally affecting driving safety. The present article reviews the impact of age-related diseases and drug treatment of these conditions on driving fitness in elderly drivers. In essence, we focus on diseases of the visual and auditory systems, diseases of the central nervous system (i.e., stroke, depression, dementia and mild cognitive disorder, and Parkinson’s disease), sleep disorders, as well as cardiovascular diseases, diabetes mellitus, musculoskeletal disorders, and frailty. We will outline the role of functional tests and the assessment of driving behavior (by a driving simulator or in real traffic), as well as the clinical interview including questions about frequency of (near) accidents, etc. in the evaluation of driving fitness of the elderly. We also address the impact of polypharmacy on driving fitness and end up with recommendations for physicians caring for older patients.

scholarly journals Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4607
Author(s):  
Dong-Woo Koh ◽  
Jin-Kook Kwon ◽  
Sang-Goog Lee
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
The Elderly ◽  
Event Related Potential ◽  
Eeg Signals ◽  
Traffic Sign ◽  
Wrong Answer ◽  
Unsupervised Machine Learning ◽  
Safe Driving ◽  
Elderly Drivers

Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.

scholarly journals Field Measurement of Naturalistic Backing Behavior

1996 ◽  
Vol 40 (18) ◽  
pp. 891-895 ◽  
Author(s):  
Jeff Harpster ◽  
Richard Huey ◽  
Neil Lerner
Keyword(s):  
Normative Data ◽  
The Elderly ◽  
Hand Position ◽  
Warning Systems ◽  
Time To Collision ◽  
Elderly Drivers ◽  
Young Drivers ◽  
The Right ◽  
Automotive System ◽  
Distance To Object

A series of observations and measurements were made as subjects drove their own vehicles in an assortment of naturalistic backing tasks conducted on public roads in real world driving conditions. As the subjects performed eight backing tasks, the following data were collected: glance direction, hand position, velocity and acceleration, and distance to object in back of the vehicle. The results provide a set of normative data usable by automotive system designers for the design of backing warning systems and other ITS applications. The results of this study were divided into glance direction, backing speed, and time-to-collision. Glance directions were found to vary greatly between tasks. Elderly drivers demonstrated more use of their mirrors and looked over their shoulder less then the young drivers. Looking over the right shoulder was the most frequent glance location across all tasks. Except for the extended backing maneuvers, backing speeds averaged around 4.8 km/h (3 mph). The maximum backing speed for the young drivers was faster than the elderly and males backed faster than females. The time-to-collision was relatively constant over the majority of the backing sequences. Minimum time-to-collision values were generally over 2 seconds.

scholarly journals Chronic subdural hematoma in patients over 65 years old: Results of using a postoperative cognitive evaluation to determine whether to permit return to driving

2021 ◽  
Vol 12 ◽  
pp. 212
Author(s):  
Masahito Katsuki ◽  
Iori Yasuda ◽  
Norio Narita ◽  
Dan Ozaki ◽  
Yoshimichi Sato ◽  
...  
Keyword(s):  
Subdural Hematoma ◽  
Cognitive Assessment ◽  
Traffic Accidents ◽  
Block Design ◽  
Behavioral Assessment ◽  
Chronic Subdural Hematoma ◽  
The Elderly ◽  
Elderly Drivers ◽  
Cutoff Values ◽  
Consciousness Disturbance

Background: Chronic subdural hematoma (CSDH) is usually associated with good recovery with burr hole irrigation and postoperative drainage under local anesthesia. In Japan, traffic accidents by the elderly drivers over 65 years old are severely increasing, and there is no consensus on whether or not to return to driving after CSDH treatment. We perform a postoperative cognitive assessment. We retrospectively investigated the return-to-driving rate and associated factors. Methods: Of the 45 patients over 65 y.o. and who had usually driven, 30 patients wished to drive again. We performed tests composed of Mini-Mental State Examination (MMSE), line cancellation and line bisection task, Kohs block design test, trail making test (TMT)-A and B, Kana-hiroi test, Rey-Osterrieth complex figure test, and behavioral assessment of the dysexecutive syndrome, in order. When all tests’ scores were better than the cutoff values, we let patients drive again. When some of the scores were worse than the cutoff values, we reevaluated the patients at the outpatient every month. If the patients’ scores could not improve at the outpatient, we recommended them to stop driving. Results: Nineteen of 30 patients could return to driving. Worse MMSE, Kohs block design test, TMT-A, TMT-B scores, higher age, dementia, or consciousness disturbance as chief complaints were associated with driving disability. Conclusion: CSDH is known as treatable dementia. However, we should perform an objective cognitive assessment before discharge because only 63% of the patients over 65 y.o. who wished to drive could return to driving.

Sign in / Sign up

Export Citation Format

Share Document