The definition of pedestrian behavior when crossing the street and facing potential collision situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in commercial vehicles. To this end, this article proposes the generation of classification models through the deployment of machine learning techniques that can predict whether there will be a collision depending on the type of reaction, the lane where it occurs, the visual acuity the level of attention, and consider the most relevant factors that determine the cognitive and movement characteristics of pedestrians. Thereby, the inclusion of this type of model in the decision-making algorithm of the AEB system allows for modulating its response. For this purpose, relevant information on pedestrian behavior is obtained through experiments made in an ad-hoc, Virtual Reality (VR) environment, using a portable backpack system in three urban scenarios with different characteristics. Database generation, feature selection, and k-fold cross-validation generate the inputs to the supervised learning models. A subsequent analysis of the accuracy, optimization, error measurement, variable importance, and classification capability is conducted. The tree-based models provide more balanced results for the performance metrics (with higher accuracy for the single decision tree case) and are more easily interpretable and adaptable to the algorithm. From them it is deduced the high importance of the reaction type and the relative position where it occurs, coinciding with the high significance of these factors in the analyzed collisions.

This paper proposes a combined system (OPREVU-AES) that integrates optimized AEB and Automatic Emergency Steering (AES) to generate evasive maneuvers, and it provides an assessment of its effectiveness when compared to a commercial AEB system. The optimized AEB system regulates the braking response through a collision prediction model. OPREVU is a research project in which INSIA-UPM and CEDINT-UPM cooperate to improve driving assistance systems and to characterize pedestrians’ behavior through virtual reality (VR) techniques. The kinematic and dynamic analysis of OPREVU-AES is conducted using CarSim© software v2020.1. The avoidance trajectories are predefined for speeds above 40 km/h, which controls the speed and lateral stability during the overtaking and lane re-entry process. In addition, the decision algorithm integrates information from the lane and the blind spot detectors. The effectiveness evaluation is based on the reconstruction of a sample of vehicle-to-pedestrian crashes (INSIA-UPM database), using PCCrash© software v. 2013, and it considers the probability of head injury severity (ISP) as an indicator. The incorporation of AEB can avoid 53.8% of accidents, with an additional 2.5–3.5% avoided by incorporating automatic steering. By increasing the lateral activation range, the total avoidance rate is increased to 61.8–69.8%. The average ISP reduction is 65%, with significant reductions achieved in most cases where avoidance is not possible.

Real world behaviour data is the most reliable reference to assess road safety in a specific road infrastructure context. However, its collection and implementation for road safety research in a rapid and portable manner is still challenging, facing data protection issues and the complexities to set up constant tracking mechanisms with their own power supply. To tackle these limitations, the Mobility Observation Box (MOB) provides a flexible data collection, to be used in subsequent video analysis. Object detection and tracking allow for the derivation of movement trajectories, which in turn allow to derive quantitative indicators of road safety relevant behaviour, namely well-established Surrogate Safety Measures (SSMs), such as Post-Encroachment-Time (PET), Time-to-Closest-Approach (TCA) and Time-to-Collision-(TTC) alongside a number of indicators like maximum speed of two interaction partners, the angle they approach each other in and the minimum distance they had at one point in their interaction. To facilitate potential MOB uses, this study leverages over 51 h of naturalistic video data at a busy Vienna intersection to advance road safety research by (i) employing random parameters binary modelling of the likelihood of critical conflict occurrence and (ii) Gaussian generalized additive spatial modelling to identify key factors influencing the absolute values of conflict angles on critical conflicts only. Within the examined intersection, specific speed and acceleration effects were determined, together with the respective heterogeneity-in-means, as well as significant categorical effects of different road user types. All road user types were ultimately less likely to be involved in safety-critical conflicts compared to cars in both leading (firstly detected) and following (secondly detected) roles, with the exception of cyclists in the leading role. Within the micro-spatial analysis, the kinematic parameters of the second road user only (speed, max acceleration and max deceleration), the duration of the interaction as well as intersection-specific local effects related to the position of the leading road user were all found to influence the transformed absolute value of the angles of critical conflicts.

The study of vulnerable road users (VRUs) behavior is key to designing and optimizing driving assistance systems, such as the autonomous emergency braking (AEB) system. These kinds of devices could help lower the VRU accident rate, which is of particular interest to cyclists, who are the subject of this research. To better understand cyclists’ reaction patterns in frequently occurring collision scenarios in urban environments, this paper focuses on developing a virtual reality (VR) simulator for cyclists (VRBikeSim) that incorporates eye-tracking functionality. The braking and steering systems were calibrated by means of on-track tests with a sensorized bicycle in order to improve the accuracy of the bicycle virtual model. From the data obtained in the virtual tests, a battery of predictive models was built using supervised machine learning classifiers. All of them exhibited an accuracy higher than 85%, especially the K-Nearest Neighbors model. This model allowed us to obtain the best balance between the prediction of avoidance and collision cases, as well as enabling computationally lower times to be incorporated into the decision-making algorithm of an AEB system.

Among the possible improvements of Autonomous Emergency Braking (AEB) systems, reducing the intensity of the automatic braking process by studying the kinematics and general behavior of the pedestrian while crossing is crucial to determine the progressiveness of the braking, or replacing part of the braking process by an evasive maneuver when a collision is imminent. This paper proposes the integration of an autonomous avoidance system (Automatic Emergency Steering, AES) that acts directly on the steering system to generate an evasive maneuver and avoid a possible pedestrian collision (OPREVU-AES system), as well as the assessment of its effectiveness compared to a commercial AEB system. OPREVU and VULNEUREA are research projects in which INSIA and CEDINT have cooperated to improve driving assistance systems and the safety of pedestrians and cyclists through Virtual Reality (VR) techniques. The analysis of the kinematic and dynamic response of the OPREVU-AES system is conducted in CarSim© software. The effectiveness evaluation procedure is based on the reconstruction of a sample of road vehicle-to-pedestrian crashes (INSIA-UPM database), using the PCCrash® software, and taking as an indicator the probability of head injury severity (ISP). The results show that the AEB system would have prevented part of the collisions, especially after the incorporation of the OPREVU-AES system. In most of the cases where avoidance is not possible, a significant reduction of the ISP is achieved.

Microsimulation is an essential tool in the planning and evaluation of large infrastructure projects or the implementation of new traffic rules for the inclusion of new road users. Efficient microsimulation on a citywide scale is provided by several software solutions, however, the representation of behaviors of unmotorized vulnerable road users (VRUs i.e., pedestrians, cyclists, scooter users) with their many degrees of freedom (including suddenly stepping on the road, crossing red lights) and their close interactions due to lack of space on sidewalks and other areas tend to be highly simplified. This work utilizes camera data collected in a busy road environment with several close interactions of all types of urban road users, to define decision-making rules and models of vulnerable road user behavior to be used in microsimulation. The simulation models are based on the observed interaction statistics, through the use of Surrogate Safety Measures (SSM, in particular Time-to-Collision-(TTC-), Post Encroachment Time- (PET-)), and the assessment of the kinematic behavior of interacting users in potential traffic conflicts.