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Aggressive driving is one of the main causes of fatal crashes. Correctly identifying aggressive driving events still represents a challenge in the literature. Furthermore, datasets available for testing the proposed approaches have some limitations since they generally (a) include only a few types of events, (b) contain data collected with only one device, and (c) are generated in drives that did not fully consider the variety of road characteristics and/or driving conditions. The main objective of this work is to compare the performance of several state-of-the-art algorithms for aggressive driving event detection (belonging to anomaly detection-, threshold- and machine learning-based categories) on multiple datasets containing sensors data collected with different devices (black-boxes and smartphones), on different vehicles and in different locations. A secondary objective is to verify whether smartphones could replace black-boxes in aggressive/non-aggressive classification tasks. To this aim, we propose the AD $^2$ (Aggressive Driving Detection) dataset, which contains (i) data collected using multiple devices to evaluate their influence on the algorithm performance, (ii) geographical data useful to analyze the context in which the events occurred, (iii) events recorded in different situations, and (iv) events generated by traveling the same path with aggressive and non-aggressive driving styles, in order to possibly separate the effects of driving style from those of road characteristics. Our experimental results highlighted the superiority of machine learning-based approaches and underlined the ability of smartphones to ensure a level of performance similar to that of black-boxes. [ABSTRACT FROM AUTHOR]

Detecting stress in computer users, while technically challenging, is of the utmost importance in the workplace, especially now that remote working scenarios are becoming ubiquitous. In this context, cost-effective, subject-independent systems are needed that can be embedded in consumer devices and classify users’ stress in a reliable and unobtrusive fashion. Leveraging keyboard and mouse dynamics is particularly appealing in this context as it exploits readily available sensors. However, available studies are mostly performed in laboratory conditions, and there is a lack of on-field investigations in closer-to-real-world settings. In this study, keyboard and mouse data from 62 volunteers were experimentally collected in-the-wild using a purpose-built Web application, designed to induce stress by asking each subject to perform 8 computer tasks under different stressful conditions. The application of Multiple Instance Learning (MIL) to Random Forest (RF) classification allowed the devised system to successfully distinguish 3 stress-level classes from keyboard (76% accuracy) and mouse (63% accuracy) data. Classifiers were further evaluated via confusion matrix, precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

The investigation of factors contributing at making humans trust Autonomous Vehicles (AVs) will play a fundamental role in the adoption of such technology. The user's ability to form a mental model of the AV, which is crucial to establish trust, depends on effective user-vehicle communication; thus, the importance of Human-Machine Interaction (HMI) is poised to increase. In this work, we propose a methodology to validate the user experience in AVs based on continuous, objective information gathered from physiological signals, while the user is immersed in a Virtual Reality-based driving simulation. We applied this methodology to the design of a head-up display interface delivering visual cues about the vehicle's sensory and planning systems. Through this approach, we obtained qualitative and quantitative evidence that a complete picture of the vehicle's surrounding, despite the higher cognitive load, is conducive to a less stressful experience. Moreover, after having been exposed to a more informative interface, users involved in the study were also more willing to test a real AV. The proposed methodology could be extended by adjusting the simulation environment, the HMI and/or the vehicle's Artificial Intelligence modules to dig into other aspects of the user experience. [ABSTRACT FROM AUTHOR]