## 4 Results
### Automation of Pit Stop Decisions Using a Neural Network
#### 4.2.2 Analysis of Feature Impact  
To examine the plausibility of the final trained NN, we checked the predictions for real-world races. We also investigated its reactions to variations in single input features and determined whether they matched expectations. An example 1-stop race with standard values according to Table 13 was created for this purpose. Three example analyses are presented in the following.  
Figure 9 shows the predictions for all three relative compounds when the driver does not make a pit stop. As expected, pit stop probabilities increase steadily until the end of the race. It is also in line with expectations that the soft compound reaches the 50% line first, followed by medium and then hard. It seems surprising that the pit stops are not generally predicted earlier, so that the values match those in Table 3. This can be explained by the fact that many pit stops, in reality, are triggered not only by tire age but also by other factors, e.g., FCY phases. There are no such triggers in our exemplary race.  
Figure 10 contains the prediction curves for a 1-, 2-, and 3-stop race respectively (varying the remaining pit stops feature). In this example, the pit stops are performed as soon as the predicted probability exceeds 50%. The soft compound is replaced by the medium compound in the first pit stop to comply with regulations. In subsequent pit stops, the soft compound is fitted. The pit stop in the 1-stop race takes place rather late on, considering that the soft compound is used in the first stint. This was already reasoned for the previous plot. In the 2-stop race, the first stop is placed at a race progress of 30%, which meets our expectations. With a length of 35% race progress each, the second and third stints are equivalently long. Knowing that the middle stint is run on medium and the last on soft, we would expect the middle stint to be longer. However, the NN does not know which compound will be fitted next, which is a disadvantage of the separated decision process. The 3-stop race decisions fit in well with expectations. The second stint on medium tires is the longest, while all others are relatively short.  
\begin{table}
\begin{tabular}{l l} \hline
**Feature** & **Standard Value** \\ \hline Race progress & [0.0, 1.0] \\ Tire age progress & [0.0, 1.0] \\ Position & 2 (pursuer) \\ Relative compound & Soft \\ Race track category & 2 \\ FCY status & 0 \\ Remaining pit stops & 1 \\ Tire change of pursuer & False \\ Close ahead & False \\ \hline \end{tabular}
\end{table}
Table 13: Standard values used to create the example race for the feature impact analysis of the pit stop decision NN.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Prediction**} \\ \cline{3-4}  & & **No pit stop** & **Pit stop** \\ \cline{3-4}
**Truth** & **No pit stop** & 57,836 & 2048 \\ \cline{3-4}  & **Pit stop** & 537 & 1855 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Confusion matrix of the predictions of the final trained pit stop decision NN on the entire database (filtered according to Section 3.2.2).  
Figure 11 shows the predictions when an SC is deployed in laps 10 and 15, each with a duration of three laps. A possible undercut situation can also be seen in laps 15 and 25. This is done by setting the tire change of pursuer feature to true in these laps along with the close ahead feature being set to true until lap 25. It can be seen that the early SC phase at a race progress around 17% does not trigger a pit stop, whereas the second one does. This is in line with expectations since, in reality, drivers often refrain from entering the pits if an SC phase is deployed at the beginning of a race, when the tires are still fresh. From the predictions with undercut attempts, we can see that the NN reacts well and decides for a pit stop if the attempt is close to a pit stop that is planned anyway.