## 3 Results
### Classification Accuracy under the Influence of Ambient Light  
In order to evaluate the classification accuracy of our system in the case that additional ambient light is present, we identically reran the experiments following the described workflow, including the training and testing. The additional ambient light was introduced by additionally switching on the installed fluorescent tubes in the utilized laboratory room (the window shades remained closed). The resulting spectrum, which is a combination of the spectrum from the LED of the VLS unit and the fluorescent tubes, is shown in Figure 8 (measured with the handheld MK350S PREMIUM spectrometer at the top of the train).  
Comparing the spectral power distribution given in Figure 8 with that of Figure 2, we can observe that the spectrum has changed quite strongly, caused by the additional light from the fluorescent tubes.  
Table 9 shows the achieved results of our system under the presence of this additional ambient light.  
The results in Table 9 outline that the additional ambient light has some negative effect, but nevertheless still shows a good classification accuracy of 99.41%. In Table 10, a summary of the occurring misclassifications is presented, where in the left column, the real (correct) class of the test scenario is given and in the right column, the predicted class of our generated random forest model is shown.  
Figure 8: Spectral power distribution of the light impinging on the surface of the train with the ambient room lighting turned on.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Total Number of** & **Number of Correct** & **Number of** & **Correct** \\
**Test Scenarios** & **Classifications** & **Misclassifications** & **Classifications** \\ \hline
2560 & 2545 & 15 & 99.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification results for the generated random forest.  
The 15 misclassifications can be broken down as follows. Of the 15 misclassifications, 7 are related to the 3M green foil in the size configurations of Area 1 and Area 2, which were misclassified (with one exemption) as 3M blue foils. Furthermore, not only were the foil and size configurations misclassified, but also the estimated speed setting was wrong. The next three misclassifications are related to the Orafol red foil in the size of Area 2. In these three cases, the identification was performed correctly, but the speed estimation failed. The same error also occurred for two runs of the scenario O yellow Area 1 Speed 1. The last three misclassifications are related to two Orafol yellow scenarios and one run of Orafol red, where, again, both the identification and speed estimation failed.  
These results show that the effect of the ambient light is twofold. On the one hand, due to the changed spectrum, the reflections of the green foil in the smallest size configurations are very similar in terms of the acquired features compared to the 3M blue foil. On the other hand, the ambient light also deteriorates the uniqueness of the Diff_Flanks feature, which is based on the clear and steep flanks originating from the ME. Nevertheless, with an overall classification accuracy of 99.41%, we can argue that our system still renders high robustness for the identification and speed estimation tasks. Additionally, the task of determining the movement direction was still performed with 100% correct results. Possible countermeasures to further improve the classification accuracy also under the presence of ambient light are discussed in Section 4.