## 3 Driver Behavior Simulation  
### Determination of Test Apparatus and Personnel  
To elucidate drivers' traffic behavior and visual patterns while navigating RTRL at interchanges and to explore the variations in vehicle speed, visual attributes, and psychological responses of drivers in these sections, which are essential for establishing parameter values in the decision sight distance calculation model, a simulation driving test was collaboratively conducted. This test utilized the UC-win/Road 9.1.0 simulation software, Forum8 driving simulation and emulation platform, and SMI ETGTM spectacle-type eye-tracking device, as depicted in Figure 7.  
The UC-win/Road software operates on the principles of computer-generated 3D virtual modeling, effectively simulating real-life scenarios and aiding in road traffic design by creating 3D road environments in diverse settings like freeways, urban streets, and tunnels. Meanwhile, the Forum8 driving simulation platform integrates a 3D road model  
Figure 6: Parameters related to LCR. Figure 7: Driver behavior simulation equipment and 3D scene modeled by UC-win/Road software. and traffic environment to provide simulated real-world driving scenarios for testing. As drivers execute designated driving maneuvers within the simulated cockpit, the platform captures their visual observations and driving experiential responses, mimicking real driving behaviors. The SMI ETGTM eyeglass eye-tracking instrument monitors the driver's visual behavior by capturing eye movement characteristics. Key instrument parameters include a sampling frequency of 120 Hz for both eyes, a tracking distance of more than 40 cm, tracking resolution of 0.1\({}^{\circ}\), gaze localization accuracy of 0.5\({}^{\circ}\), and horizontal and vertical field of view angles of 120\({}^{\circ}\) and 92\({}^{\circ}\), respectively. The precision of the test outcomes and subsequent conclusions is directly contingent upon the judicious selection of the test sample size, thereby significantly influencing data collection and result interpretation. Following statistical principles, this experiment employed a two-sided test methodology to ascertain the optimal number of participants required. The calculation of this figure is determined using Equation (8). \[n=2\left[\frac{\left(t_{\alpha}+t_{2\beta}\right)S}{\delta}\right]^{2} \tag{8}\]  
where \(n\) denotes sample size, \(\alpha\) is the significance level, which was taken as 0.05 in the test, so the confidence level coefficient \(t_{\alpha}\) is calculated as 1.96. \(\beta\) represents the probability of committing the second type of error, as indicated by related research, when \(1-\beta\geq 0.75\), the sample data can reflect the overall the test \(\beta\) takes 0.1, \(t_{2\beta}=1.282\), \(S\) signifies the standard deviation, \(\delta\) is the difference between the tolerance error and the check, the test \(\delta\) takes 9.3. Based on Equation (8), the calculated minimum sample size necessary for the test amounted to 10 individuals. To ensure both the test's validity and practicality, considering the gender distribution and driving age of drivers [30], a selection of 12 male drivers and 4 female drivers was made. These participants boasted an average driving experience of 3 years and exhibited a visual acuity of 1.0 or higher. Moreover, to guarantee the quality of the driver eye-gaze data, subjects aged between 23 and 35 years were specifically chosen. Detailed information regarding the test subjects is provided in Table 3.  
To maintain the precision of the test results, the following prerequisites were stipulated for the subjects:  
* Subjects were required to be in a normal physiological condition before commencing the test.