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Abstract-Various factors are believed to cause blinking. One of these factors is attention. However, the relationship between blinking and attention has not yet been elucidated. In this study, we focus on electrodermal activity (EDA) to demonstrate this relationship. EDA is an electrical phenomenon involving the glandular releasing of sweat that is caused by mental excitement or strain. We employ EDA to quantify attention as a means to elucidate the blinking-attention relationship. We conduct an experiment based on EDA and present the results to verify this relationship.

This paper proposes a real-time eyelid state recognition method based on a video sequence. The human eye strongly reflects the mental state of an individual, such as attention, drowsiness, stress and confusion. In recent times, the automatic identification of such mental states using non-contact eyelid state recognition technology is proving to be a promising avenue for the development of such systems. In the field of Intelligent Transport Systems (ITS), high accuracy and real-time processing are necessary for detecting driver drowsiness in order to prevent accidents. To develop such a recognition method, we use Higher-order Local Auto-Correlation (HLAC). HLAC can represent the shape feature in images clearly without incurring a high computational cost, and it implements position invariability. A Support Vector Machine (SVM) is used to differentiate between open and closed eyelids on the basis of the HLAC feature. The proposed method can achieve open and closed eyelid recognition rates of 98.77% and 98.98%, respectively. We also verify the real-time processing capabilities of our method, thus confirming that it is effective for eyelid state recognition.

Electrodermal Activity (EDA) refers to change in the electrical properties of skin during mental exertion caused by tension and agitation. In recent studies, it has been revealed that there is an overlap between the brain activation area where emotion occurs and the brain activation area responsible for attentiveness. Consequently, in this paper we test our hypothesis that, given these findings, visual attention can be estimated via EDA. The results of experiments conducted verify that EDA can be used as an attention index.

Various factors are believed to cause blinking. One of these factors is attention. However, the relationship between blinking and attention has not yet been elucidated. In this study, we focus on electrodermal activity (EDA) to demonstrate this relationship. EDA is an electrical phenomenon involving the glandular releasing of sweat that is caused by mental excitement or strain. We employ EDA to quantify attention as a means to elucidate the blinking-attention relationship. We conduct an experiment based on EDA and present the results to verify this relationship.

A control method for passing through an intersection for automated vehicles is proposed. If automated vehicles approaching an intersection communicate with each other and schedule the time of entering the intersection by small deceleration and acceleration, the vehicles can pass the through the intersection without stopping thereby enabling energy-saving by reducing unnecessary deceleration. This study proposes a method of categorizing intersection patterns, and extracting the required conditions for the realization of a non-stop intersection for each intersection pattern, performing control to realize such an intersection and transmitting information to vehicles entering the intersection for the creation of a virtual platoon. The proposed method is validated by experiments at an intersection of one-way traffic in single lanes.

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In this study, a new method was developed to automate vehicles. Numerous vehicle control systems have been realized for electronic steering, driving, and braking. Higherstandard vehicle control systems have increased costs. This study focused on cloud vehicle control using roadside sensors and computers. If a high-standard vehicle control system can be realized by using vehicles with no sensors or computers, this can create a new service model for transport. The key point was to obtain state quantities of vehicles and to maintain vehicle-toroadside communication. The proposed method obtains state quantities of vehicles with roadside sensors by using the iterative closest point algorithm. The vehicle positions are correlated with communication information. The control quantities of the vehicles are received by roadside computers. Experiments using electric vehicles showed that the proposed method can realize high-standard vehicle control for self-driving vehicles.