Figure 1.
Example of a curvy road where indirect glare from the side rearview mirror is experienced by the preceding driver. ©2021 Microsoft Corporation, ©2021 TomTom.
Figure 1.
Example of a curvy road where indirect glare from the side rearview mirror is experienced by the preceding driver. ©2021 Microsoft Corporation, ©2021 TomTom.
Figure 2.
Comparison of pixel masking and matrix beam showing the difference of two simulation algorithms: (a,c) ADB simulation using matrix beam algorithm, in which each segment can be switched on/off individually; (b,d) ADB simulation using pixel-masking algorithm, which uses one beam pattern, with the boundary box of the stimulus vehicle masked out.
Figure 2.
Comparison of pixel masking and matrix beam showing the difference of two simulation algorithms: (a,c) ADB simulation using matrix beam algorithm, in which each segment can be switched on/off individually; (b,d) ADB simulation using pixel-masking algorithm, which uses one beam pattern, with the boundary box of the stimulus vehicle masked out.
Figure 3.
A graphical comparison in the simulator between the original and the modified ADB simulation, in which the non-glare zone is set at 100% and all parameters are identical. The red lines around the stimulus vehicle represent the boundary box: (a,c) The original ADB simulation in which the three-dimensional boundary box is recognized, and, thus, the entire vehicle geometry is covered by the non-glare zone. The modification in (b,d) simulates a more realistic ADB, in which only the taillight is recognized; thus, in top view (d), it is just a line at the rear of the stimulus vehicle. The non-glare zone is highlighted with yellow dashed lines showing that, with the modification, the side rearview mirror will be excluded from the boundary box during the cornering maneuver. The comparison shows that the simulator’s modified AFS plug-in can reproduce the glare issue.
Figure 3.
A graphical comparison in the simulator between the original and the modified ADB simulation, in which the non-glare zone is set at 100% and all parameters are identical. The red lines around the stimulus vehicle represent the boundary box: (a,c) The original ADB simulation in which the three-dimensional boundary box is recognized, and, thus, the entire vehicle geometry is covered by the non-glare zone. The modification in (b,d) simulates a more realistic ADB, in which only the taillight is recognized; thus, in top view (d), it is just a line at the rear of the stimulus vehicle. The non-glare zone is highlighted with yellow dashed lines showing that, with the modification, the side rearview mirror will be excluded from the boundary box during the cornering maneuver. The comparison shows that the simulator’s modified AFS plug-in can reproduce the glare issue.
Figure 4.
Effect on glare when altering non-glare zone width, showing that widening the non-glare zone could effectively eliminate glare: (a) 100% non-glare zone width (glare); (b) 150% non-glare zone width (no glare).
Figure 4.
Effect on glare when altering non-glare zone width, showing that widening the non-glare zone could effectively eliminate glare: (a) 100% non-glare zone width (glare); (b) 150% non-glare zone width (no glare).
Figure 5.
Effect on road illumination when altering the non-glare zone width, showing that a widened non-glare zone sacrifices road illumination. For example, the traffic signs circled on (a) are well illuminated; however, when the non-glare zone increases on (b), the signs are included in the non-glare zone and, thus, are not illuminated. This indicates the driver will have better and wider visibility with the narrower non-glare zone, which enhances driving safety.
Figure 5.
Effect on road illumination when altering the non-glare zone width, showing that a widened non-glare zone sacrifices road illumination. For example, the traffic signs circled on (a) are well illuminated; however, when the non-glare zone increases on (b), the signs are included in the non-glare zone and, thus, are not illuminated. This indicates the driver will have better and wider visibility with the narrower non-glare zone, which enhances driving safety.
Figure 6.
Driving scenarios prepared for the virtual night drive data collection. The curve varies from 25 m to 100 m, with the 25 m curvature corresponding to a vehicle traveling at minimum ADB deactivation speed, below which the ADB will automatically be deactivated and switch to low beam. The 100 m curvature corresponds to the minimum curvature that NHTSA proposed for ADB testing in NPRM [
22].
Figure 6.
Driving scenarios prepared for the virtual night drive data collection. The curve varies from 25 m to 100 m, with the 25 m curvature corresponding to a vehicle traveling at minimum ADB deactivation speed, below which the ADB will automatically be deactivated and switch to low beam. The 100 m curvature corresponds to the minimum curvature that NHTSA proposed for ADB testing in NPRM [
22].
Figure 7.
The position of the virtual light sensor, which corresponds to the ADB test fixture from SAE J3069 [
26].
Figure 7.
The position of the virtual light sensor, which corresponds to the ADB test fixture from SAE J3069 [
26].
Figure 8.
Lux reading of S-curve with 25 m curvature simulation at 100% non-glare zone width. The dashed line is 18.9 lux, above which is considered to be glare, according to both NHTSA [
22] and SAE [
26].
Figure 8.
Lux reading of S-curve with 25 m curvature simulation at 100% non-glare zone width. The dashed line is 18.9 lux, above which is considered to be glare, according to both NHTSA [
22] and SAE [
26].
Figure 9.
S-curve with 25 m curvature simulation at 100% non-glare zone width: (
a) At the beginning of the simulation, the vehicle travels on a straight road, and no glare is created. (
b) When the vehicle enters the corner, glare is created, corresponding to the first peak in
Figure 8. (
c) When the stimulus vehicle exits the high beam area, there is a short time when glare drops to zero, corresponding to the area between two peaks in
Figure 8. (
d) Light penetrates through the vehicle body due to the limitation of the simulator, which is shown as the third peak in
Figure 8 and is neglected as an error.
Figure 9.
S-curve with 25 m curvature simulation at 100% non-glare zone width: (
a) At the beginning of the simulation, the vehicle travels on a straight road, and no glare is created. (
b) When the vehicle enters the corner, glare is created, corresponding to the first peak in
Figure 8. (
c) When the stimulus vehicle exits the high beam area, there is a short time when glare drops to zero, corresponding to the area between two peaks in
Figure 8. (
d) Light penetrates through the vehicle body due to the limitation of the simulator, which is shown as the third peak in
Figure 8 and is neglected as an error.
Figure 10.
Lux reading of S-curve with 25 m curvature simulation in different non-glare zone widths, showing the trend of reduction in glare with increasing non-glare zone width.
Figure 10.
Lux reading of S-curve with 25 m curvature simulation in different non-glare zone widths, showing the trend of reduction in glare with increasing non-glare zone width.
Figure 11.
Total glare time vs. non-glare zone width for S-curve with 25 m curvature, showing that the total glare time reduces with increasing non-glare zone width. All glare is eliminated if the non-glare zone widens to 256% of the boundary box width.
Figure 11.
Total glare time vs. non-glare zone width for S-curve with 25 m curvature, showing that the total glare time reduces with increasing non-glare zone width. All glare is eliminated if the non-glare zone widens to 256% of the boundary box width.
Figure 12.
Schematic showing fuzzy logic controller block diagram.
Figure 12.
Schematic showing fuzzy logic controller block diagram.
Figure 13.
Membership function of fuzzy logic controller: (a) road illumination input triangular membership function in Trial 1; (b) change in non-glare zone width output triangular membership function in Trial 1; (c) road illumination input Gaussian membership function in Trial 2; (d) change in non-glare zone width output Gaussian membership function in Trial 1.
Figure 13.
Membership function of fuzzy logic controller: (a) road illumination input triangular membership function in Trial 1; (b) change in non-glare zone width output triangular membership function in Trial 1; (c) road illumination input Gaussian membership function in Trial 2; (d) change in non-glare zone width output Gaussian membership function in Trial 1.
Figure 14.
The minimum curvature in the driving scenario vs. best non-glare zone width.
Figure 14.
The minimum curvature in the driving scenario vs. best non-glare zone width.
Table 1.
Parameters of the driving scenario.
Table 1.
Parameters of the driving scenario.
Curvature [m] | Side Friction Coefficient [-] | Speed [km/h] | Rounded Speed [km/h] | Following Distance [m] |
---|
25 | 0.18 | 23.9 | 25 | 20 |
50 | 0.17 | 32.8 | 30 | 25 |
75 | 0.17 | 40.2 | 40 | 30 |
100 | 0.16 | 45.1 | 45 | 40 |
Table 2.
Parameters of the unscaled membership functions, range 0–1.
Table 2.
Parameters of the unscaled membership functions, range 0–1.
| Input | Output |
---|
Function names | good | bad | increase | good | decrease |
Trimf parameters | [0, 1, 2] | [−1, 0, 1] | [0.5, 1, 1.5] | [0, 0.5, 1] | [−0.5, 0, 0.5] |
Gaussmf parameters | [0.4247, 1] | [0.4247, 0] | [0.2123, 1] | [0.2123, 0.5] | [0.2123, 0] |
Table 3.
Variable range of the membership functions.
Table 3.
Variable range of the membership functions.
| Input | Output |
---|
Driving Scenario | Glare | Road Illumination | Change in Non-Glare Zone Width |
---|
S-curve with 25 m curvature | 0–8.144 | 100–256 | −100–100 |
S-curve with 50 m curvature | 0–14.686 |
S-curve with 75 m curvature | 0–15.047 |
S-curve with 100 m curvature | 0–9.61 |
Table 4.
Trial 1 iteration of four driving scenarios, using the fuzzy logic controller with linear membership function.
Table 4.
Trial 1 iteration of four driving scenarios, using the fuzzy logic controller with linear membership function.
Driving Scenario | Iteration | Non-Glare Zone Width [%] | Glare Rating [s] | Output [%] |
---|
S-curve with 25 m curvature | 1 | 256 | 0 | −105 |
2 | 151 | 4.2 | −2.3 |
3 | 149 | 4.4 | −0.53 |
4 | 148 | 4.5 | −0.06 |
S-curve with 50 m curvature | 1 | 180 | 0 | −20 |
2 | 160 | 3 | −7.93 |
3 | 152.3 | 4.6 | −0.885 |
4 | 151 | 4.8 | −0.008 |
S-curve with 75 m curvature | 1 | 154 | 0 | −9.34 |
2 | 144.7 | 1.7 | −5.2 |
3 | 138.4 | 3.1 | −1.17 |
4 | 137.2 | 3.6 | −0.23 |
5 | 137 | 3.6 | 0.274 |
S-curve with 100 m curvature | 1 | 143 | 0 | −3.87 |
2 | 139.1 | 1.1 | −2.82 |
3 | 136.3 | 1.2 | −2 |
4 | 134.3 | 1.8 | −0.785 |
5 | 133.5 | 1.9 | −0.5 |
Table 5.
Trial 2 iteration of four driving scenarios, using the fuzzy logic controller with Gauss membership function.
Table 5.
Trial 2 iteration of four driving scenarios, using the fuzzy logic controller with Gauss membership function.
Driving Scenario | Iteration | Non-Glare Zone Width [%] | Glare Rating [s] | Output [%] |
---|
S-curve with 25 m curvature | 1 | 256 | 0 | −55.7 |
2 | 200.3 | 2 | −25.1 |
3 | 175.2 | 2.6 | −7.09 |
4 | 168.11 | 3.1 | −2.36 |
5 | 165.75 | 3.2 | −1.86 |
6 | 163.89 | 3.4 | 0.367 |
S-curve with 50 m curvature | 1 | 180 | 0 | −13.9 |
2 | 166.1 | 2 | −7.6 |
3 | 158.5 | 2.6 | −4.86 |
4 | 153.64 | 4.2 | −1.64 |
5 | 152 | 4.3 | −1.13 |
6 | 150.87 | 4.5 | −0.308 |
S-curve with 75 m curvature | 1 | 154 | 0 | −4.74 |
2 | 149.26 | 1 | −3.54 |
3 | 145.72 | 1.6 | −2.75 |
4 | 142.97 | 2.2 | −2 |
5 | 140.97 | 2.6 | −1.44 |
6 | 139.53 | 2.8 | −1.15 |
7 | 138.38 | 3.1 | −0.67 |
S-curve with 100 m curvature | 1 | 143 | 0 | −2.62 |
2 | 140.38 | 0.5 | −2.16 |
3 | 138.22 | 0.9 | −1.71 |
4 | 136.51 | 1.22 | −1.3 |
5 | 135.21 | 1.4 | −1 |
6 | 134.21 | 1.6 | −0.7 |
7 | 133.51 | 1.75 | −0.45 |
Table 6.
Best non-glare zone width vs. minimum curvature of the S-curve in all four driving scenario simulations.
Table 6.
Best non-glare zone width vs. minimum curvature of the S-curve in all four driving scenario simulations.
Driving Scenario | Minimum Curvature [m] | Trial 1 Optimal Non-Glare Zone Width [%] | Trial 2 Optimal Non-Glare Zone Width [%) | Glare-Free Non-Glare Zone Width [%] |
---|
1 | 25 | 148 | 164 | 256 |
2 | 50 | 151 | 150 | 180 |
3 | 75 | 137 | 138 | 154 |
4 | 100 | 133 | 133 | 143 |