## 5 Discussion  
The evaluation of the precision and recall performance metrics is crucial for future implementation of classification models within decision algorithms in commercial AEB systems. Precision is defined as the ratio between the number of true positives and the sum of true positives and false positives, while recall is the ratio between true positives and the sum of true positives and false negatives. Statistically, precision measures the ability to identify the relevant data points and recall to identify all relevant observations within the sample. For observations labelled as \"Collision\", a higher accuracy of the model implies that it predicts fewer cases of collision, as it identifies them as avoidance. If recall is higher for this category, then it predicts more collision cases in those situations where the accident does not occur. The same comparison applies to the \"Avoidance\" class, where higher precision predicts more crash cases and higher recall predicts more avoidance cases. In particular, the trade-off between these two metrics would imply that the system efficiently identifies collision and avoidance cases. Under this premise, it would be plausible to adapt the classifiers studied to regulate emergency braking as a function of the kinematic variables of both subjects, including the cyclist's observation component. Additionally, precision and recall are jointly evaluated through the F1-score, which allows us to analyze the balance between these two measures of relevance. In practice, it is preferable for the overall precision to be higher, as in this case, the AEB system would act more conservatively. It is more convenient to perform full braking when there is no risk of collision than to perform partial braking when there is a potential collision situation. Percentage values of the performance metrics are shown below in Figure 21. The RF and KNN models are the most accurate within the battery of supervised classifiers evaluated in this paper, although the KNN model shows a better balance between the precision and recall metrics. Figure 21: Performance metrics of supervised classifiers. In the case of the RF model, the largest imbalance occurs for the \"Avoidance\" category. The precision value is considerably higher than the recall value, implying that this model is particularly focused on reducing the number of false positives at the cost of obtaining a smaller number of true positives. The lower recall value for this class confirms that the system classifies a smaller number of relevant instances out of all possible instances or, equivalently, there is a larger number of false negatives. Therefore, RF would certainly be a less conservative model if it were incorporated in the decision algorithm of an AEB system, since it would predict fewer collision cases than can actually occur; note how the precision value is slightly higher than recall, which confirms this analysis. On the other hand, the KNN model captures fewer false positives from the \"Avoidance\" class and fewer false negatives from the \"Collision\" class, which is indicative of the fact that this classifier broadly predicts more collisions than avoidances. The distribution of the precision and recall metrics is the same in the individual DT and the SVC models as in the RF and KNN models, respectively. However, both individual DT and SVC are less accurate, although the latter achieves a better balance than the DT. The F1-score value for the \"Collision\" class is similar in all supervised classifiers but is higher in the models with higher accuracy. The ROC (receiving operating characteristic) curves show a trade-off between the true positive rate (sensitivity) and the false positive rate (1-specificity). (Figure 22). SVC exhibits a higher AUC (area under the curve) value, although the distribution of points on the curve of this classifier is quite similar to those corresponding to the RF and KNN models for high values of the discrimination threshold.