Evaluation of Cognitive Distraction in a Real Vehicle Based on the Reflex Eye Movement

Son, Le Anh; Suzuki, Tatsuya; Aoki, Hirofumi

doi:10.20485/jsaeijae.9.1_1

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…This data can be used to simulate eye movement based on head movement such as [2] , [3] , Obinata group [4] , [5] , [6] , [7] , [8] , [9] , Anh Son et al [10] , [11] , [12] , [13] , [14] , [15] , and so on. In addition, this data can use to see the effect of a visual stimulus or mental workload on driver performance as well as eye movement.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

SmartEye and Polhemus data for vestibulo–ocular reflex and optokinetic reflex model

Son

Aoki

2018

Data in Brief

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In this data article, this dataset included raw data of head and eye movement that collected by Polhemus (Polhemus Inc) and SmartEye (Smart Eye AB) equipment. Subjects who have driver license participated in this experiment. The experiment was conducted with a driving simulator that was controlled by CarSim (Mechanical simulation Co., Anna Arbor, MI) with the vehicle motion. This data set not only contained the eye and head movement but also had eye gaze, pupil diameter, saccades, and so on. It can be used for the parameter identification of the vestibulor-ocular reflex (VOR) model, simulation eye movement, as well as running other analysis related to eye movement.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

SmartEye and Polhemus data for vestibulo–ocular reflex and optokinetic reflex model

Son

Aoki

2018

Data in Brief

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“…As for visual distraction, cognitive distraction can be characterized by indicators of both driving performance and eye movements [ 141 ], including (1) vehicle-based indicators, such as speed [ 179 ], wheel steering [ 169 ], lane discipline [ 169 , 179 , 180 ], and braking behavior [ 181 ], and (2) driver-based, behavioral indicators, such as gaze parameters (e.g., fixation duration, glance frequency, and gaze distribution) [ 181 , 182 , 183 , 184 ] and head orientation. A driver makes significantly fewer high-speed saccadic eye movements and spends less time looking to the relevant periphery for impending hazards with increasing complexity of the secondary task(s).…”

Section: State 3: Distractionmentioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

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“…Another potential method is to use the involuntary eye movements based on the vestibulo-ocular reflex model that are simulated by head movements or by vibrations from the moving vehicle. In this method, differences between predicted and actual eye simulation are assessed as a measure of mental workload (Obinata et al, 2008 , 2009 , 2010 ; Aoki et al, 2015 ; Anh Son et al, 2016 , 2017a , b , c , d , e , 2018 ; Le and Aoki, 2018 ; Son and Hirofumi, 2018 ; Son et al, 2018 ) However, the use of eye information to measure mental workload still has some limitations, such as oversensitivity to light, vibration, noise, and visual information.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy

Son

Aoki

Murase

et al. 2018

Front. Hum. Neurosci.

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Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.

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Evaluation of Cognitive Distraction in a Real Vehicle Based on the Reflex Eye Movement

Cited by 5 publications

References 17 publications

SmartEye and Polhemus data for vestibulo–ocular reflex and optokinetic reflex model

SmartEye and Polhemus data for vestibulo–ocular reflex and optokinetic reflex model

Survey and Synthesis of State of the Art in Driver Monitoring

A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy

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