## 2 State of the Art  
To increase the validity and generalizability of the results obtained from user interaction with the virtual environment, the 3D scenario should be as realistic and immersive as possible. Indeed, predominant technologies in the use of virtual reality techniques, such as Head-Mounted Displays (HMDs), offer greater portability and stereoscopic rendering capabilities, leading to superior user immersion with the interface [4,5]. In conjunction with other VR-related technologies, it is possible to easily collect physiological and/or biometric data. The deployment of motion capture [4] allows for recording the movements of the user's limbs and other body parts for their subsequent inclusion and analysis in the digital animation of an avatar. The use of portable backpack devices extends the physical limits of the areas where users are allowed to move, resulting in more natural behavior. Other external tracking systems may complement the collation of relevant data to different degrees of detail (e.g., eye trackers and haptic devices for hand or finger tracking). The major issue reported by users while using HMD is cybersickness, a general discomfort caused by the discrepancy between simulated visual motion and the perception of movement from the vestibular system [5]. Detecting and quantifying its effects on individuals is important to establish the validity of the user's VR experience. To this end, frameworks such as the Simulator Sickness Questionnaire (SSQ) [6] and the Fast Motion Sickness Scale (FMS) have been proposed. On the other hand, the System Usability Scale (SUS) is used to measure usability in VR environments. Usability is defined as the effectiveness, intuitiveness, and satisfaction with which specified users can achieve specified goals in particular environments, particularly interactive systems [7]. Pedestrian behavior, in addition to being defined by variables related to their relative movement with respect to the vehicle (position, speed, acceleration, and trajectory), is also strongly associated with visual perception [8, 9, 10]. Eye-tracking technologies embedded in Virtual Reality equipment allow the real-time recognition and detection of the objects at which the pedestrian is looking. This information may be employed to identify the main sources of distraction [11] that can compromise the response to a hazard on the road. Furthermore, visual perception affects the estimation of speeds and distances relative to other objects/vehicles. For instance, depth perception [12] influences the calculation of the time needed to cross a street when an automobile is approaching a crosswalk, since the pedestrian evaluates the distance to that vehicle and the width of the roadway to adjust the crossing speed. Furthermore, ref. [13] demonstrated that pedestrians tend to overestimate Time-to-Collision (TTC, time required for two objects to collide if they continue at their current speed and along the same path) at higher approaching vehicle speeds, resulting in riskier time gaps. This effect is more noticeable in VR scenarios due to the restriction of the field of view (FOV) [14]. Concerning the characterization of the pedestrian's behavior, ref. [15] explores the main crossing patterns at signalized intersections, focusing on the most significant factors affecting pedestrian compliance behavior and the reasons for pedestrian non-compliance, as well as the possibility of interacting with vehicles under certain traffic conditions. Other studies focus on the estimation of reaction times and the observation of different speeds when crossing the road as a function of age and/or gender [16,17]. To this end, the application of Machine Learning Techniques (MLT) is expected to bring new insights regarding which features have the most promising predictive power to avoid traffic collisions. In this respect, some research is focused on the development of multilevel predictive models for vehicle-to-pedestrian collision avoidance using clustering and classification techniques [18].