## 2 State of the Art
Nevertheless, most of the recent literature using supervised machine learning techniques are studies of vehicle-cyclist interactions at the macroscopic level. Papers such as [19] classify whether it is a risk conflict or not. Others, such as [20], focus on identifying the main risk patterns of bike riders through tree-structured models, using a similar approach to that evaluated in this paper in the exploratory analysis and by means of Individual Decision Tree and Random Forest models. In addition to this type of classifier, models such as Support Vector Machine (SVM) or AdaBoost are applied in papers such as [21; 22] to determine which pre-crash behaviors most affect the severity of an accident, establishing a framework for evaluating unsafe decision-making. Recent examples based on deep learning at the vehicular level are presented in [23; 24], in which several implementation approaches for pedestrian and cyclist intention detection and determination by autonomous vehicles, through processing of on-board camera-based videos and different variants of convolutional neural networks (CNN), are discussed. Although some studies such as [25] include prototypes based on region-based CNN for real-time detection of collisions with vulnerable users, the implementation of predictive methods based on neural networks has wider coverage in the macroscopic assessment of vehicle-cyclist interactions, and not so much at the level of implementation in active safety systems in vehicles. The most widespread resource used is that of big data sources from maps and satellite images, from the application of multi-layer gradient descent back-propagation error models [26] to more advanced methods based on generative adversarial networks (GANs) [27]. Therefore, most machine learning predictive models of cyclist collisions have been implemented in macroscopic traffic studies, while most of the applications at the vehicular level are based on convolutional networks for the detection and processing of video captured by cameras. The objective is, therefore, to generate a predictive collision model that can be implemented in the decision-making algorithm of a commercial AEB system. This process is similar to the one conducted by the authors in previous research [28] applied to vehicle-to-pedestrian conflicts. There is a double scientific contribution of this research as follows: (i) The proposed models are based on supervised machine learning classifiers, with low computational cost by only processing kinematic data and not requiring image processing through feature extractors; (ii) It is a microscopic study of vehicle-cyclist interactions, based on cutting-edge techniques, such as virtual reality, through a calibrated simulator that guarantees a high level of immersion and an exhaustive assessment of the cyclist's critical actions. A predictive collision model would allow for emergency braking to be regulated by applying partial braking pressure in cases where the VRU actions are predicted not to lead to an accident. This approach was used in [29], where the authors developed a prototype based on an AEB system with braking control using a predictive pedestrian collision model and an automatic evasive steering (AES) system. The results showed that the optimization enabled an increase in the avoidance effectiveness of software-reconstructed accidents of up to 78% and an average injury severity probability (ISP) of 65%. The partial reduction in the braking pressure enabled increasing the minimum distance and time gap to avoid rear-end collisions in emergency braking situations. Automatic brake pressure regulation in an AEB system ensures less wear and tear in the braking system over time and increases the distance and time gap with following vehicles when executing the deceleration process.