The transport sector is experiencing a fast and revolutionary development, moving towards the deployment of autonomous vehicles. Thus, the accuracy of the position information and the integrity of the navigation system have become key factors. In this paper, we address the problem of the enhancement of the integrity of the position provided by a Global Navigation Satellite System receiver by exploiting sensor fusion. To this aim, we estimate the lateral offset and heading of the vehicle with respect to a georeferenced roadway centerline from the images supplied by an on-board camera. Moreover, we perform integrity monitoring based on the implementation of the Solution Separation in the parity space. The numerical results indicate that the use of sensor fusion and digital map allows to attain a longitudinal Protection Level reduction with respect to the case in which sensor fusion is not exploited. More specifically, a decrease of about 70% is achieved when a single constellation is used, while reduction is less relevant, about 15%, when two constellations are employed.

The rapid development of Virtual Reality (VR) technology has led the industry and research community to look at its major challenges with increased interest. The main challenge in ensuring a high Quality of Experience (QoE) for users is represented by cybersickness, a phenomenon similar to motion sickness experienced by many VR users, while at the same time, the high data rates needed by VR require the definition of traffic models for network optimization. These two problems are intertwined, but have never been studied jointly before due to the lack of suitable datasets. In this paper, we present Questset, the first dataset designed for this purpose. Questset contains over 40 hours of VR traces from 70 users playing commercially available video games, and includes both traffic data for network optimization, and movement and user experience data for cybersickness analysis. Therefore, Questset represents an enabler to jointly address the main VR challenges in the near future.

In virtual reality applications, head-mounted displays allow users to explore virtual surroundings, thus creating a high sense of immersion. However, due to the novelty of the technology and the possibility of freely enjoying a $$360^\circ $$360 ∘ virtual world, users can get distracted and divert their attention from the content of the application. In this work, we define a set of guidelines for the design of virtual reality applications for enhancing the users’ attention. To the best of our knowledge, this is one of the first attempts to provide general guidelines for virtual application design based on visual attention. More specifically, we analyze the different categories of factors that contribute to the user’s responsiveness and define a set of experiments for measuring the user’s promptness with respect to visual stimuli with different features and in the presence of audio/visual distractions. Experimental tests have been carried out with 36 volunteers. The users’ reaction time has been recorded and the performed analysis allowed the definition of a set of guidelines based on individual, operational, and technological factors for the design of virtual reality applications optimized in terms of user attention. In particular, statistical tests demonstrated that the presence of distractions leads to significantly different reaction times with respect to the case of no distractions, and that users belonging to different age intervals have significantly different behaviors. Moreover, the optimal placement of objects has been identified and the impact of cybersickness has been analyzed.

Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.