## 3 Results
### Determining the Applicable Generated Random Forest Model  
In our work, we showed that in both experimental settings (firstly, the LED of the VLS unit is the only active light source and, secondly, in addition, also the room lighting is switched on), our general approach to utilize retroreflective foils in combination with a specular reflecting mirror element yields a high number of correct results. The movement direction was determined with 100% correctness in both settings; for the tasks of identification and speed estimation, the accuracies were 99.96% and 99.41% in terms of correct classifications. This raises the question of whether our system can also determine which generated random forest model has to be applied for the classification: either the one trained when only the LED was active, or the model trained when additional ambient light was present. For determining the correct model to be applied, we can reuse the data from the movement direction determination task (see Section 2.4). Since we can determine the movement direction of the train with 100% correct results, this gives us the opportunity to know when no meaningful reflections from the train were acquired. For example, we know that when the train was moving in the Forward direction, the reflections from the train occur after Frank 2 (see Figure 7) and consequently, before Flank 1 only reflections of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Correct Class** & **Predicted Class of the Random Forest Model** \\ (**Ground Truth**) & \\ \hline
3M\_green Area 1 Speed 1 & 3M\_blue Area 1 Speed 2 \\
3M\_green Area 1 Speed 2 & 3M\_green Area 2 Speed 4 \\
3M\_green Area 2 Speed 3 & 3M\_blue Area 1 Speed 2 \\
3M\_green Area 2 Speed 4 & 3M\_blue Area 1 Speed 4 \\ O\_red Area 2 Speed 2 & O\_red Area 2 Speed 1 \\ O\_red Area 2 Speed 2 & O\_red Area 2 Speed 1 \\ O\_red Area 4 Speed 1 & 3M\_red Area 1 Speed 1 \\ O\_yellow Area 1 Speed 1 & O\_yellow Area 1 Speed 2 \\ O\_yellow Area 2 Speed 1 & 3M\_green Area 1 Speed 1 \\ O\_yellow Area 2 Speed 2 & O\_yellow Area 1 Speed 1 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of the misclassifications.  
the environment are present. For a Backward moving train, this fact, of course, is the other way round. Therefore, we can calculate a parameter that yields us the presence or absence of ambient light. To illustrate the validity of this approach, please see Figure 9 that shows a scatter plot of calculated mean values for the green channel in the two settings, when it is known that only the reflections from the environment are present. The two settings are named \"only LED\" (blue scatter points) and \"Ambient\" (red scatter points) given on the y-axis and the calculated mean values, called mean_value_empty, over 100 samples on the x-axis.  
This scatter plot shows the clear distinguishability of the two settings. This consequently proves that by calculating the mean value during the known absence of the train can be used to determine whether only the LED was on or if, additionally, the ambient room lighting also was switched on. As a consequence of this clear distinguishability, we can also infer, based on this parameter, the applicable model of the random forest and generally that our system is also able to perform the task of ambient light determination.