The fusion of Internet of Things (IoT), Digital Twins, and Virtual Reality (VR) technologies marks a pivotal advancement in urban development, offering new services to citizens and municipalities in urban environments. This integration promises enhanced urban planning, management, and engagement by providing a comprehensive, real-time digital reflection of the city, enriched with immersive experiences and interactive capabilities. It enables smarter decision-making, efficient resource management, and personalized citizen services, transforming the urban landscape into a more sustainable, livable, and responsive environment. The research presented herein focuses on the practical implementation of a DT concept for managing cross-domain smart city services, leveraging VR technology to create a virtual replica of the urban environment and IoT devices. Imperative for cross-domain city services is interoperability, which is crucial not only for the seamless operation of these advanced tools but also for unlocking the potential of cross-service applications. Through the deployment of our model at the IoTMADLab facilities, we showcase the integration of IoT devices within varied urban infrastructures. The outcomes demonstrate the efficacy of VR interfaces in simplifying complex interactions, offering pivotal insights into device functionality, and enabling informed decision-making processes.

Understanding pedestrians’ cognitive processes in traffic environments is crucial for developing strategies to enhance safety and reduce accidents. This study assesses the efficacy of virtual reality (VR) in evaluating pedestrian behavior in simulated road-crossing scenarios. It investigates VR’s capability to realistically mimic the cognitive load experienced in real-world settings. It examines the technical integration of VR with psychophysiological recording to capture cognitive demand indicators accurately. Utilizing a dedicated VR application and electroencephalogram (EEG) measurements, this research aims to elicit significant Event-Related Potentials (ERP), like P3 and Contingent Negative Variation (CNV), associated with decision-making processes. The initial results demonstrate VR’s effectiveness in creating realistic environments for investigating cognitive mechanisms and the balance between induced immersion and experienced discomfort. Additionally, the tasks involving time-to-arrival estimations and oddball scenarios elicited the anticipated components related to attentional and decision-making processes. Despite increased discomfort with extended VR exposure, our results show that it did not negatively impact the cognitive workload. These outcomes highlight VR’s efficacy in replicating the cognitive demands of real-world settings and provide evidence to understand the neurophysiological and behavioral dynamics of vulnerable road users (VRUs) in traffic scenarios. Furthermore, these findings support VR’s role in behavioral and neurophysiological research to design specific safety interventions for VRUs.

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.

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.

INTRODUCTION. Over the last few years, the number of pedestrian fatalities on urban roads has increased, largely due to infractions associated with their behaviors (e.g., crossing when traffic lights are red). It is argued that these behaviors reflect a lack of risk perception. Road safety programs have tried to raise awareness through various methods, using quite often emotionally powerful experiences (e.g. testimonies of people who have experienced an accident themselves). Recently, Virtual Reality (VR) has been deployed with the aim of increasing the efficacy of these safety programs. Previous studies have demonstrated the potential of VR to improve pedestrian safety, especially when it is accompanied bydebriefing and critical reflection. METHOD. A total of 43 participants (M = 24.5 years old; SD = 5.14; 65.12% female) were involved in an experimental study with a 2x2 factorial design and pre-post measures. They were randomly assigned to one of four groups (Experience a VR accident /Experience VR without an accident; having a debriefing after the VR experience/not having a debriefing after the VR experience). Pre-post measures were of two kinds, (a) self-report measures and (b)VR behavioral measures. Multivariate Analysis of Variance (MANOVA) and general linear mixed models (GLMM) were used to analyze the data. RESULTS AND DISCUSSION. The main results revealed that (a) participants reported a general reduction in the number of rules violations, regardless of condition, and (b) there was a significant reduction in the number of violations committed in VR (i.e., crossing when the traffic light is red) in the condition where participants had previously experienced an accident. These results support the potential of using VR environments to improve pedestrian safety-related behavior. Implications for future research are delineated.