Crash dieting: The effects of eating and drinking on driving performance

Previous research suggests that compared to mobile phone use, eating and drinking while driving is more common and is seen as lower risk by drivers. Nevertheless, snacking at the wheel can affect vehicle control to a similar extent as using a hands-free phone, and is actually a causal factor in more crashes. So far, though, there has not been a controlled empirical study of this problem. In an effort to fill this gap in the literature, we used the Brunel University Driving Simulator to test participants on a typical urban scenario. At designated points on the drive, which coincided with instructions to eat or drink, a critical incident was simulated by programming a pedestrian to walk in front of the car. Whilst the driving performance variables measured were relatively unaffected by eating and drinking, perceived driver workload was significantly higher and there were more crashes in the critical incident when compared to driving normally. Despite some methodological limitations of the study, when taken together with previous research, the evidence suggests that the physical demands of eating and drinking while driving can increase the risk of a crash

Cognitive Load Detection For Advanced Driver Assistance Systems

In this thesis, we investigate cognitive load detection and classification based on minimally invasive methods. Cognitive load detection is crucial for many emerging applications such as advanced driver assistance systems (ADAS) and industrial automation. Numerous studies in the past have reported several psychological measures, such as eye-tracking, electrocardiogram (ECG), electroencephalogram (EEG), as indicators of cognitive load. However, existing physiological features are invasive in nature. Consequently, the objective of this study is to determine the feasibility of non-invasive features such as pupil dilation measurements low-cost eye-tracker with minimal constraints on the subject for cognitive load detection. In this study, data from 33 participants were collected while they underwent tasks that are designed to permeate three different cognitive difficulty levels with and without cognitive maskers and the following measurements were recorded: eye-tracking measures (pupil dilation, eye-gaze, and eye-blinks), and the response time from the detection response task (DRT). We also demonstrate the classification of cognitive load experienced by humans under different task conditions with the help of pupil dilation and reaction time. Developing a model that can accurately classify cognitive load can be used in various sectors such as semi-autonomous vehicles and aviation. we have used a data fusion approach by combining pupil dilation and DRT reaction time to determine if the classification accuracy increases. Further, we have compared the classifier with the highest classification accuracy using data fusion against the accuracy of the same classifier with only one feature (pupil dilation; reaction time) at a time

Driver frustration detection from audio and video in the wild

We present a method for detecting driver frustration from both video and audio streams captured during the driver's interaction with an in-vehicle voice-based navigation system. The video is of the driver's face when the machine is speaking, and the audio is of the driver's voice when he or she is speaking. We analyze a dataset of 20 drivers that contains 596 audio epochs (audio clips, with duration from 1 sec to 15 sec) and 615 video epochs (video clips, with duration from 1 sec to 45 sec). The dataset is balanced across 2 age groups, 2 vehicle systems, and both genders. The model was subject-independently trained and tested using 4-fold cross-validation. We achieve an accuracy of 77.4% for detecting frustration from a single audio epoch and 81.2% for detecting frustration from a single video epoch. We then treat the video and audio epochs as a sequence of interactions and use decision fusion to characterize the trade-off between decision time and classification accuracy, which improved the prediction accuracy to 88.5% after 9 epochs

A Secondary Assessment of the Impact of Voice Interface Turn Delays on Driver Attention and Arousal in Field Conditions

Voice interface use has become increasingly popular in vehicles. It is important that these systems divert drivers’ attention from the primary driving task as little as possible, and numerous efforts have been devoted to categorizing demands associated with these systems. Nonetheless, there is still much to be learned about how various implementation characteristics impact attention. This study presents a secondary analysis of the delay time between when users finish giving commands and when the system responds. It considers data collected on 4 different production vehicle voice interfaces and a mounted smartphone in field driving. Collapsing across systems, drivers showed an initial increase in heart rate, skin conductance level, and off-road glance time while waiting for a system to respond; a gradual decrease followed as delays continued. The observed attentional and arousal changes are likely due to an increase in anticipation following a speech command, followed by a general disengagement from the interface as delay times increase. Safety concerns associated with extended delay times and suggestion of an optimal range for system response times are highlighted

Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload

Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs.Comment: 42 pages, 5 figures, 3 Table

Mental workload and visual impairment: differences between pupil, blink, and subjective rating

Este experimento tiene dos objetivos: 1) Estudiar la validez concurrente de tres medidas de carga mental, la escala de juicios NASA TLX, la dilatación de la pupila y la tasa de parpadeo, poniendo a prueba la hipótesis de que, en situaciones de tarea única, arrojan resultados convergentes, pero, en doble tarea, arrojan resultados disociativos. 2) Analizar su capacidad para predecir el deterioro en la búsqueda visual. Las tres medidas fueron analizadas con las mismas tareas cognitivas realizadas en condiciones de tarea simple y de doble tarea (tarea cognitiva y búsqueda visual) en un experimento intrasujetos con veintinueve participantes. Las medidas de carga mental mostraron validez concurrente en las condiciones de tarea única, pero en las condiciones de doble tarea apareció un patrón de resultados complejo que sugiere que NASA TLX consistiría en la adición subjetiva de los juicios de cada tarea; la dilatación de la pupila mediría la activación promedio que subyace a las tareas cognitivas; y la tasa de parpadeo produciría efectos contrapuestos: mientras que la carga mental de las tareas cognitivas incrementa la tasa de parpadeo, las demandas visuales la inhiben. Las tres medidas fueros buenos predoctores del deterioro visual. Se discute la justificación del uso de estas medidas en el campo aplicado de la conducción y otras actividades.This research has two aims: (a) To study the concurrent validity of three measures of mental workload, NASA TLX rating scale, pupil dilation and blink rate, testing the hypothesis that they will provide convergent results using a single-task, and dissociative results for dual-task; and (b) To analyse their capability to predict visual search impairment. These three measures were analyzed in the same cognitive tasks in singletask and dual-task (cognitive task and visual search) conditions in a within-subjects experiment with twenty-nine participants. Mental workload measures showed concurrent validity under single-task condition, but a complex pattern of results arose in the dualtask condition: it is suggested that NASA TLX would be a subjective addition of the rating of each task; pupil dilation would measure the average arousal underlying the cognitive tasks; and the blink rate would produce opposite effects: whereas mental workload of cognitive tasks would increase blink rate, visual demand would inhibit it. All three measures were good predictors of visual impairment. The soundness of these measures is discussed with regard to the applied field of driving and other activities

Virtual training for assembly tasks: a framework for the analysis of the cognitive impact on operators

The importance of training for operators in industrial contexts is widely highlighted in literature. Virtual Reality (VR) technology is considered an efficient solution for training, since it provides immersive, realistic, and interactive simulations environments where the operator can learn-by-doing, far from the risks of the real field. Its efficacy has been demonstrated by several studies, but a proper assessment of the operator’s cognitive response in terms of stress and cognitive load, during the use of such technology, is still lacking. This paper proposes a comprehensive methodology for the analysis of user’s cognitive states, suitable for each kind of training in the industrial sector and beyond. Preliminary feasibility analysis refers to virtual training for assembly of agricultural vehicles. The proposed protocol analysis allowed understanding the operators’ loads to optimize the VR training application, considering the mental demand during the training, and thus avoiding stress, mental overload, improving the user performance

Frustration recognition from speech during game interaction using wide residual networks

ABSTRACT Background Although frustration is a common emotional reaction during playing games, an excessive level of frustration can harm users’ experiences, discouraging them from undertaking further game interactions. The automatic detection of players’ frustration enables the development of adaptive systems, which through a real-time difficulty adjustment, would adapt the game to the user’s specific needs; thus, maximising players experience and guaranteeing the game success. To this end, we present our speech-based approach for the automatic detection of frustration during game interactions, a specific task still under-explored in research. Method The experiments were performed on the Multimodal Game Frustration Database (MGFD), an audiovisual dataset—collected within the Wizard-of-Oz framework—specially tailored to investigate verbal and facial expressions of frustration during game interactions. We explored the performance of a variety of acoustic feature sets, including Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs), as well as the low dimensional knowledge-based acoustic feature set eGeMAPS. Due to the always increasing improvements achieved by the use of Convolutional Neural Networks (CNNs) in speech recognition tasks, unlike the MGFD baseline—based on Long Short-Term Memory (LSTM) architecture and Support Vector Machine (SVM) classifier—in the present work we take into consideration typically used CNNs, including ResNets, VGG, and AlexNet. Furthermore, given the still open debate on the shallow vs deep networks suitability, we also examine the performance of two of the latest deep CNNs, i. e., WideResNets and EfficientNet. Results Our best result, achieved with WideResNets and Mel-Spectrogram features, increases the system performance from 58.8 % Unweighted Average Recall (UAR) to 93.1 % UAR for speech-based automatic frustration recognition