Identification of driver cognitive workload using support vector machines with driving performance, physiology and eye movement in a driving simulator

Son, Joonwoo; Oh, Hyungseok; Park, Myoungouk

doi:10.1007/s12541-013-0179-7

Cited by 32 publications

(18 citation statements)

References 16 publications

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“…There are several existing studies that use multimodal information to measure cognitive load [32, 33]. We cannot compare the numerical results of our study with the numerical results of these studies because of differences in experimental design, populations, and measured signals.…”

Section: Discussionmentioning

confidence: 94%

“…We can, however, compare our study with the existing studies to understand the effect of multimodal information in cognitive load measurement. Son et al collected three modalities of information - physiological, gaze, and performance information, for cognitive load measurement in a driving simulator [32]. In their study, the best accuracy using the three-modality information was higher than the best accuracy using each single modality for cognitive load measurement.…”

Section: Discussionmentioning

confidence: 99%

“…Hussain et al tested k-nearest neighbor (KNN) with different k values in measuring cognitive load using face, physiology, and task performance data [31]. Different kernel functions of support vector machine (SVM), including linear kernel [31] and Gaussian kernel [32], were used in cognitive load measurement. Novak et al analyzed linear discriminant analysis (LDA), diagonal LDA, and stepwise LDA to classify cognitive load [33].…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Zhang

Wade

Bian

et al. 2017

IEEE Trans. Affective Comput.

107

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Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes—feature level fusion, decision level fusion and hybrid level fusion—were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Zhang

Wade

Bian

et al. 2017

IEEE Trans. Affective Comput.

107

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“…On the other hand, physiological measures have been proposed as useful metrics for assessing driver's workload. Studies found that a near linear increase in heart rate and skin conductance appeared as the workload levels of drivers increases [5]. In order to understand which methodology should be adopted as a suitable measure of a driver's social behaviour, it is imperative to first understand different behaviors possible and their causes.…”

Section: Driver Behaviour Overviewmentioning

confidence: 99%

Driver's Behaviour Monitoring on Urban Roads of a Tier 2 City in India

Mishra

Bajaj

2015

2015 7th International Conference on Emerging Trends in Engineering &Amp; Technology (ICETET)

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There are significant differences in individual driving patterns of drivers due to each one's varying social behaviours.A driver's incorrect perception and misinterpretation of true traffic scenario owing to his social behavioural approach causes significant problem for road traffic safety as it leads to degraded performance of driving pattern. Hence there is an urgent need to take measures and develop modules which work for the reduction of road traffic accidents. This paper proposes an approach which will try to analyze the factors which affect the social behavior of a driver. An observational study was carried out in the city of Nagpur with the help Also live observations were undertaken during daylight hours by installing CCTV cameras to monitor traffic. The observations mainly tried to focus on whether drivers were engaged in any visible type of secondary activity while driving or involved in any type of traffic violation because of aggressive pattern or casual approach. The observations were quite alarming and based on results some approaches have been proposed which help to analyze the relationship between factors causing driving errors and traffic accidents.

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“…et al, 2013) Advanced vehicle motion control delegation Fan et al, 2014;Meng & Sun, 2014;Vaitkus et al, 2014;Weyer et al, 2015; A c c e p t e d M a n u s c r i p t 88 al., 2012;Haberstroh et al, 2010;Hess et al, 2013;Jung & Qin, 2013;Kim & Son, 2011;Kim et al, 2011;Kim et al, 2012;Manseer & Riener, 2014;MarinLamellet & Haustein, 2014;Masala & Grosso, 2013;Meng & Sun, 2014;Moussa et al, 2012;Munro et al, 2010;Nakagawa & Park, 2014;Nakano et al, 2013;Rödel et al, 2014;Schall et al, 2012;Son et al, 2013;Taib et al, 2013;Vardaki & Karlaftis, 2011;Vhaduri et al, 2014;Vitabile et al, 2011;Williams et al, 2014;Young & Bunce, 2011) Infotainment Note. Springer Link and ACM Digital Library did not support the metadata search function, so we picked the articles related to HCI/HVI from the potentially related articles.…”

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confidence: 99%

Research Issues in Smart Vehicles and Elderly Drivers: A Literature Review

Rhiu

Kwon

Bahn

et al. 2015

International Journal of Human-Computer Interaction

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In this paper, we reviewed both studies on general smart car technologies and HCI/HVI studies that were published in journals and conferences, so that we can identify the current status of research and suggest future research directions. Furthermore, we reviewed previous studies on elderly drivers as they could be the most vulnerable social group in terms of new technology acceptance. A total of 257 articles for HCI research and 45 articles for elderly drivers were selected and reviewed from 11,267 collected articles (from 2010 to 2014). According to the results, most articles were mainly related to safety and adaptive features (e.g., driver's state recognition, vehicle surrounding monitoring, driver action-suggestion), and infotainment research in terms of HCI (e.g., IT devices-vehicle interaction, vehicle-vehicle interaction) was relatively insufficient despite its high research demand. According to the results of the literature Downloaded by [New York University] at 03:59 29 July 2015 A c c e p t e d M a n u s c r i p t 2 review and technological trends analysis based on previous technical roadmaps, from HCI/HFE perspectives, research related to 'Assistance systems', 'Physiological & mental state recognition', 'Position sensor technology', 'Behavior recognition', and 'Infotainment' were suggested to HCI/HFE researchers for the further research. In particular, HCI/HFE researchers need to focus on research on acceptable levels of automation, observing new driving behaviors, investigation of driver characteristics to develop personalized services, and new technology acceptance to develop and improve smart cars in the future.

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Identification of driver cognitive workload using support vector machines with driving performance, physiology and eye movement in a driving simulator

Cited by 32 publications

References 16 publications

Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Driver's Behaviour Monitoring on Urban Roads of a Tier 2 City in India

Research Issues in Smart Vehicles and Elderly Drivers: A Literature Review

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