Investigation of Driving Behavior and Cognitive Ability Concerning Planning Process during Driving of Elderly Drivers

Shino, Motoki; Nakanishi, Michinobu; Imai, Reo; Yoshitake, Hiroshi; Fujita, Yoshio

doi:10.20485/jsaeijae.9.3_138

Cited by 3 publications

(7 citation statements)

References 7 publications

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“…To evaluate the on-road driving ability of healthy older individuals on the basis of vehicle behaviors, we used an instrumented automatic vehicle with a data recorder, charge-coupled device (CCD) cameras, and dual brake controls ( Figures 1A,B ; Shino et al, 2018 ). The data recorder (Tough More-eye S manufactured by Finefit Design, Aichi, Japan) provides vehicle information, including speed, acceleration, and gas and brake pedal positions.…”

Section: Methodsmentioning

confidence: 99%

“…A previous study analyzing more than 8,000 traffic accidents reported that no serious accidents occurred when the speed of the vehicle was below 5 km/h ( Kröyer, 2015 ). Further, we adopted the same cut-off value (5 km/h) in our previous study assessing the driving ability of older people based on the speed of the vehicle at intersections ( Shino et al, 2018 ; Yamamoto et al, 2020 ). Therefore, in the present study, participants who drove a vehicle below 5 km/h at the intersection were classified as safe drivers.…”

Section: Methodsmentioning

confidence: 99%

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Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People

Yamamoto

Yamagata

Hirano

et al. 2020

Front. Aging Neurosci.

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In developed countries, the number of traffic accidents caused by older drivers is increasing. Approximately half of the older drivers who cause fatal accidents are cognitively normal. Thus, it is important to identify older drivers who are cognitively normal but at high risk of causing fatal traffic accidents. However, no standardized method for assessing the driving ability of older drivers has been established. We aimed to establish an objective assessment of driving ability and to clarify the neural basis of unsafe driving in healthy older people. We enrolled 32 healthy older individuals aged over 65 years and classified unsafe drivers using an on-road driving test. We then utilized a machine learning approach to distinguish unsafe drivers from safe drivers based on clinical features and gray matter volume data. Twenty-one participants were classified as safe drivers and 11 participants as unsafe drivers. A linear support vector machine classifier successfully distinguished unsafe drivers from safe drivers with 87.5% accuracy (sensitivity of 63.6% and specificity of 100%). Five parameters (age and gray matter volume in four cortical regions, including the left superior part of the precentral sulcus, the left sulcus intermedius primus [of Jensen], the right orbital part of the inferior frontal gyrus, and the right superior frontal sulcus), were consistently selected as features for the final classification model. Our findings indicate that the cortical regions implicated in voluntary orienting of attention, decision making, and working memory may constitute the essential neural basis of driving behavior.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People

Yamamoto

Yamagata

Hirano

et al. 2020

Front. Aging Neurosci.

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“…1a,b). 20 The data recorder (Tough More-Eye S, Finefit Design, Aichi, Japan) provides vehicle information, such as the speed, acceleration, and the gas and brake pedal positions. Charge-coupled device cameras (n = 4) were placed on the windshields, front dashboard, and pedal box.…”

Section: On-road Driving Testmentioning

confidence: 99%

“…In general, drivers need to notice the stop sign and decelerate the vehicle immediately and then pass through the intersection at low speed. To assess such driving behaviors objectively, we evaluated the two parameters considered in our previous study, 20 namely the minimum speed of a vehicle moving past the stop line to the entrance of the intersection and the speed of the vehicle at the entrance of the intersection (see details in Fig. 1c).…”

Section: Classification Of a Safe Or Unsafe Drivermentioning

confidence: 99%

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Machine‐learning approach to predict on‐road driving ability in healthy older people

Yamamoto

Hirano

Yoshitake

et al. 2020

Psychiatry Clin Neurosci

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Aim In Japan, fatal traffic accidents due to older drivers are on the rise. Considering that approximately half the older drivers who have caused fatal accidents are cognitively normal healthy people, it has been required to detect older drivers who are cognitively normal but at high risk of having fatal traffic accidents. However, a standardized method for assessing the driving ability of older drivers has not yet been established. We thus aimed to identify a new sensing method for the evaluation of the on‐road driving ability of healthy older people on the basis of vehicle behaviors. Methods We enrolled 33 healthy older individuals aged over 65 years and utilized a machine‐learning approach to dissociate unsafe drivers from safe drivers based on cognitive assessments and a functional visual acuity test. Results The linear support vector machine classifier successfully dissociated unsafe drivers from safe drivers with accuracy of 84.8% (sensitivity of 66.7% and specificity of 95.2%). Five clinical parameters, namely age, the first trial of the Rey Auditory Verbal Learning Test immediate recall, the delayed recall of the Rey–Osterrieth Complex Figure Test, the result of the free‐drawn Clock Drawing Test, and maximal visual acuity, were consistently selected as essential features for the best classification model. Conclusion Our findings improve our understanding of clinical risk factors leading to unsafe driving and may provide insight into a new intervention that prevents fatal traffic accidents caused by healthy older people.

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White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people

et al. 2022

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Aim: Healthy older drivers may be at high risk of fatal traffic accidents. Our recent study showed that volumetric alterations in gray matter in the brain regions within the dorsal attention network (DAN) were strongly related to the risk of unsafe driving in healthy older people. However, the relationship between white matter (WM) structural connectivity and driving ability in healthy older people is still unclear. Methods:We used diffusion tensor imaging to examine the association between microstructural alterations in the DAN and the risk of unsafe driving among healthy older people. We enrolled 32 healthy older individuals aged over 65 years and screened unsafe drivers using an on-road driving test. We then determined the pattern of WM aberrations in unsafe drivers using tract-based spatial statistics.Results: The analysis demonstrated that unsafe drivers had significantly higher axial diffusivity values in nine WM clusters compared with safe drivers. These results were primarily observed bilaterally in the dorsal superior longitudinal fasciculus, which is involved in the DAN. Furthermore, correlation analyses showed that higher axial diffusivity values in the superior longitudinal fasciculus were associated with lower Trail Making Test A scores within unsafe drivers. This result suggests that functionally, WM microstructural alterations in the DAN are associated with attention problems, which may contribute to the risk of unsafe driving among healthy older people. Conclusion: Our findings may elucidate the neurobiological mechanisms underlying the increased risk of unsafe driving in healthy older people, potentially facilitating the development of new interventions to prevent fatal accidents.

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Investigation of Driving Behavior and Cognitive Ability Concerning Planning Process during Driving of Elderly Drivers

Cited by 3 publications

References 7 publications

Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People

Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People

Machine‐learning approach to predict on‐road driving ability in healthy older people

White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people

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