## 3 Results  
In the following, the data evaluation based on the study in the light tunnel is described, since it is identical for both test environments. At the key points, the results for the closed test site are also discussed. The data evaluation starts with the determination of the object luminance \(L_{\text{O}}\) and the background luminance \(L_{\text{B}}\) (mean value of the background luminances of the four surrounding areas) from the recorded luminance images, which are shown exemplarily for the light tunnel on the left side and for the closed test site on the right side in Figure 7. The luminance images are recorded in rows, so that one luminance image per PWM level is sufficient for the evaluation of all five or six object positions in the respective row. From the obtained data for object luminance \(L_{\text{O}}\) and background luminance \(L_{\text{B}}\), the luminance difference \(\Delta L\) and Weber contrast \(C_{\text{W}}\) are calculated by Equation (1). In addition, the threshold luminance difference \(\Delta L_{\text{th}}\) is determined via the STV model (Equation (2)) and then the Visibility Level is determined using Equation (3). The results of the photometric evaluation of the Weber contrast \(C_{\text{W}}\) are shown in Figure 8 as an example for position 1, which is located at a distance of 40 m in front of the vehicle and a horizontal offset of \(-4.50\) m from the longitudinal axis of the vehicle. From Figure 8, it can be observed that both the object luminance \(L_{\text{O}}\) and the background luminance \(L_{\text{B}}\) increase linearly from a PWM level of 45 (see Figure 8 on the left). This causes the luminance difference \(\Delta L\) between the object and its environment to also increase linearly. Due to this linearity, the contrast curve, as shown in Figure 8 on the right, enters saturation with an increasing PWM level. This relationship can be described by calculating the Weber contrast \(C_{\text{W}}\) with the linearly increasing luminances in Equation (6). \[C_{\text{W}}(\text{PWM})=\frac{L_{\text{O}}(\text{PWM})-L_{\text{B}}(\text{PWM})}{L_{\text{B}}(\text{PWM})}=\frac{\Delta L(\text{PWM})}{L_{\text{B}}(\text{PWM})}=\frac{a\cdot\text{PWM}}{b\cdot\text{PWM}}=\frac{a}{b}=\text{const.} \tag{6}\]  
As Equation (6) shows, the course of the luminance difference \(\Delta L\) can be described as a multiple \(a\) of the set PWM level. This is also the case for the background luminance \(L_{\text{B}}\), which is linked to the set PWM level via the constant slope \(b\). Thus, in the linear range of the luminance curve, a constant Weber contrast \(C_{\text{W}}\) results, which can be represented as the ratio of the two slopes, \(a\) and \(b\). Figure 7: Exemplary representation of the recorded luminance images for the light tunnel (**left**) and the closed test site (**right**); the fixation vehicle, which is positioned behind the last row of objects in the driving direction, can be observed in both figures. Since the Weber contrast \(C_{\text{W}}\) enters a saturation range and thus does not allow for differentiation at higher PWM levels, the Visibility Level shown in Figure 9 on the right is used for further evaluation. As Figure 9 shows, the Visibility Level increases with the increasing PWM level and thus enables a differentiated view over the entire adjustable intensity range of the LED headlamps of the test vehicle in the further evaluation. Thus, the Visibility Level generated by the various control of the LED headlights at the different object positions represents the independent variable. The dependent variable is the detection probability of the gray card at a given Visibility Level. This is calculated by determining the proportion of drivers who detected the gray card at the respective Visibility Level. Since no random positive responses are to be expected, the correlation is calculated with the simplified psychometric function according to [PERSON] et al. (2013) from Equation (5).