## 3 Segment Optimization for Adaptive Driving Beam
### Optimized Segment Distribution  
Since it is obviously impossible to show all optimization results, Figure 7 shows an example of such an optimized segment distribution for a (3 \(\times\) 24)-segment high beam.  
Inspection of this figure clearly illustrates the initial hypothesis that the more vehicles are recognized under a certain angle, the smaller these segments need to be. Since the highest frequency of registered objects was found near the center of the light distribution, this is where the smallest segments are found as well. The largest segments were found all the way at \(\pm\)20\({}^{\circ}\) horizontally and 2\({}^{\circ}\) upwards vertically. (One could also argue that no light is actually necessary under those angle positions because it would be unlikely that a driver needs to respond to an object at these positions.)  
In order to check if the optimization led to any benefit, all nonoptimized light segment distributions as well as all optimized light distributions were tested against a set of 2000 test frames. For each of these frames randomly selected from the country road and motorway data, the number of active segments was recorded and the percentage of the illuminated area was calculated. This is shown in Figure 8.  
Figure 7: Optimized segment distribution for a (3 \(\times\) 24)-segment adaptive driving (high) beam.  
While both curves seem to be rather similar, it is directly obvious that the optimized segment distribution always performed at least as well or better than the conventional distribution by having a larger proportion of road area being illuminated to a higher level. While this was not a perfect test to ensure that the optimization algorithm was ideal, the fact that the algorithm resulted in more illumination directed to safety-critical locations and the fact that the performance became asymptotic beyond 100-1000 segments were very promising that the algorithm's sensitivity and accuracy was sufficient for this purpose. The high variance for certain segment numbers is explained by the possibility to form different patterns with a certain number of pixels. One hundred pixels can, for example, be set to a (10 \(\times\) 10)- or a (2 \(\times\) 50)-segment distribution. For those cases, the lowest, highest, and mean illumination levels are shown.  
The second thing to note in this figure is that the higher the number of segments, the lesser the impact of such an optimized light distribution.  
Furthermore, the figure also shows a diminishing return of illuminated road area for headlamps with more than 1000 segments. However, this is only for the purpose of illuminating objects to be seen along the road. Such higher-segment headlights can of course bring other benefits in the form of a high-resolution road projection or light-based driver assistance systems.  
Overall, the data here show that there can be certain benefits to the overall approach. However, the theoretical analysis as shown in [22] has highlighted more potential with higher projected benefits by the optimization. The general way the optimized segments are sorted, however, remains the same as predicted. Nevertheless, it was important to show that this approach also works with real-life data.