## 4 Proposed Simulation System  
In this paper, we consider FL to implement the proposed simulation system. The proposed system structure is shown in Figure 3. In the implemented system, sensors will detect and quantify the input parameters for the FL-based system. We implement two models: DALM1 and DALM2. The input parameters of DALM1 include driving experience (DE), in-car environment conditions (IECs), and driver age (DA), while for DALM2, we add a new parameter called the accident anxiety state (AAS). For both models, the output parameter is the DAL. We explain each parameter in the following paragraphs. Figure 2: Factors affecting driver’s state. * Driving Experience (DE)  
In general, it is known that experienced drivers are able to make more appropriate responses than inexperienced drivers. Also, the driving experience can affect the traffic safety behavior. Specifically, young people have peculiar driving behavior such as driving faster, decelerating and accelerating more abruptly, being less likely to come to a full stop at stop signs and tailgating other cars, in comparison to middle-aged and older drivers [25]. * In-car Environment Conditions (IECs)  
The IECs are related to the vehicle conditions such as the air quality aspects influencing environmental conditions in vehicles. Especially with the mobile age and associated lengthy periods of stay in enclosed cars, maintaining healthy air hygiene in the car interior is very important. There are different factors that influence the vehicle environment such as temperature, window-opening, the correct usage of automated air conditioning systems, and indoor air filters. These factors are useful for improving the environmental conditions of vehicles and optimizing the interior air hygiene. These are related also with the different endowments, ages and models of vehicles [26]. * Driver Age  
The terms set for input and output parameters are defined as shown in Table 1. For the implementation of the proposed system, we consider triangular and trapezoidal membership functions as shown in Figure 4 because they are good for real-time systems and applications [29]. The membership functions are shown in Figure 5. We set the DE, IEC, AAS and DAL values to range from 0 to 1 unit. The DE value range we set from 20 to 90 considering the driver ages in Japan [25]. The DAL is considered in 6 levels for DALM1 and 7 levels for DALM2. When the DAL value is DAL1, the amount of the driver's anxiety is good, while when it is DAL6 (DALM1) or DAL7 (DALM2), this is the worst case. If the DE is NG, the IECs are Ba and the DA is Yo, then the DAL is DAL6 (Rule 1 of Table 2), or If the AAS is Hi, the DE is VG, the IECs are Gd and the DA is Ol, then the DAL is DAL3 (Rule 81 of Table 3). We explain the membership functions as follows. The triangular membership function is denoted by \(f()\), while the trapezoidal membership function is denoted by \(g(0)\). The maximum value for the triangular membership function is denoted by x\({}_{0}\), while the edges are denoted by x\({}_{0}-\)a\({}_{0}\) and x\({}_{0}+\)a\({}_{1}\), for the left and right sides, respectively. For the trapezoidal membership function, the maximum values are denoted by x\({}_{0}\) and x\({}_{1}\), while \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Term Sets** \\ \hline Driving Experience (DE) & Not Good (NG), Good (G), Very Good (VG) \\ In-car Environment Conditions (IECs) & Bad (Ba), Normal (Nor), Good (Gd) \\ Driver Age (DA) & Young (Yo), Middle (Mi), Old (Ol) \\ Accident Anxiety State (AAS) & Low (Lo), Middle (Mid), High (Hi) \\ DALM1: Driver Anxiety Level (DAL) & Driver Anxiety Level1 (DAL1), DAL2, DAL3, DAL4, \\ DAL5, DAL6 & DAL1, DAL2, DAL3, DAL4, DAL5, DAL6, DAL7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters and term set. Figure 4: Triangular and trapezoidal membership functions.