## 6 Conclusions  
In this research, different approaches to classification models have been evaluated to determine the existence of a potential collision, given a series of factors that define pedestrian crossing behavior. These predictors have been extracted and calculated from data obtained from tests in urban scenarios in VR where users had to face potential hit-and-run situations. The integration of VR technology in the assessment of pedestrian behavior's main patterns when crossing an urban road is a worthwhile method. To this end, the movement of vehicles and pedestrians walking on sidewalks or crossing the road, the main architectural elements and street furniture are integrated to attain a high level of detail. This realistic environment has allowed numerous pedestrian tests to be carried out under safe and repeatable conditions. The questionnaires used during the VR tests show that users experienced a greater increase in discomfort after 55 min. Most of the participants assessed the VR system usability as \"acceptable\" and they evaluated the effect of immersion as \"consistent\". For the KNN and SVM models, an optimization function has been implemented to determine the hyperparameters that maximize their accuracy. For the Random Forest ensembled tree model, the OOB error criterion was used, and the importance of the variables was additionally measured. From the results obtained, it is concluded that all the models offer an acceptable accuracy, higher than 80%. If the labeling performance of each class is considered, the tree models offer more balanced values in terms of accuracy and sensitivity. However, despite achieving high accuracy in all the classification models, it is not enough to make the decision algorithm of the ADAS system depend solely and exclusively on the output generated by these classification approaches, but it would complement the algorithm since the result obtained in real-time from these models can be used to regulate the braking response of the AEB itself. Thus, in cases where collision avoidance is predicted, the braking pressure can be a fraction of the maximum pressure, or the braking progressivity can be increased, delaying in time the point of maximum deceleration. Therefore, the ML model included in the AEB is complementary and never a substitute, always guaranteeing braking safely in a potential collision situation. Figure 19: ROC curves and AUC value of each classifier. Another noteworthy and differentiating aspect is the internal structure of the models. Both SVM and KNN are \"black-box\" models, while tree models can be categorized as \"white-box\" [46]. Commonly, black-box models are those containing complex mathematical functions (SVM) and those requiring a deep understanding of the distance function and spatial representation (KNN). White-box models are preferred for practical application since the decision algorithm is very similar to that of human language. Given the characteristics of the problem, the accuracy obtained and that a model where it is possible to identify in a less straightforward way how the algorithm behaves and how it should be tuned is more profitable for future implementation in a logic system of an AEB device, the individual decision tree is the most preferable method for this purpose. As a reference, [47] proposes a similar approach, also based on variables related to the pedestrian's movement and posture, captured by an onboard front camera. In this case, the response variable is based on the hazard determination (safe, danger), and the judgment is equivalent. The results obtained in terms of accuracy and recall for the technique used (in this case, fuzzy rules) are 94% and 87%, respectively. Compared to the results obtained in this research, the ML methods are used to achieve a higher trade-off between precision and recall, achieving a similar final accuracy in the case of the individual decision tree.