Designing a Car-Driver's Cognitive Process Model for considering Degree of Distraction

Designing a Car-Driver's Cognitive Process Model for considering Degree of Distraction

Taku Harada (Tokyo University of Science, Chiba, Japan), Kazuaki Mori (Tokyo University of Science, Chiba, Japan), Akira Yoshizawa (Denso IT Laboratory, Inc., Tokyo, Japan) and Hirotoshi Iwasaki (Denso IT Laboratory, Inc., Tokyo, Japan)
DOI: 10.4018/IJSSCI.2015070101
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A distracted state of a driver affects car driving state. The eye tracking can reveal an individual's psychological state. In this paper, we design a driver's cognitive process model by clearly indicating the relations between cognitive states, such as perception and memory, in the process to produce the driving action using the eye tracking data. It is important to consider degree of distraction. Therefore, we consider a cognitive distraction expressed both serially and quantitatively in the model. In this modeling, we utilize a production system framework, and the cognitive distracted state is managed by a module in the production system.
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Some research (Salvucci, 2006; Xiao-huma, Shi-quan, & Zi-jian, 2011; Cao, & Wang, 2010) has expressed a driver’s cognitive process without a black-box by using ACT-R cognitive architecture (Anderson, Bothell, Byrne, Douglass, Lebiere, & Qin, 2004). In these study, the term “cognitive process” refers to the relationships of cognitive states, such as consciousness and memory, in a driving action and decision-making. In these studies, driving state is expressed by modeling a cognitive process; however, distraction is not taken into consideration. Therefore, we believe that the driving state is not accurately analyzed in these studies. Research analyzing the distracted states of drivers has also been conducted (Liang, Reyes, & Lee, 2007; Liang, & Lee, 2010). However, in these studies, the distracted state is determined based only on whether the driver is distracted; therefore, the distracted state is not expressed correctly. Further, the distractions’ influences on driving are not considered.

In contrast, some research has taken distraction into account, in relation to the expressed driving state (Salvucci, 2002; Salvucci, 2013). However, these studies utilized a simulated environment that imitates the driving environment, including the driver’s actions. For example, a driver’s operation of a monitor is performed in a simulated environment. Thus, distraction cannot be accurately evaluated; therefore, driving states cannot be accurately expressed.

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