AutoEmotive: bringing empathy to the driving experience to manage stress

With recent developments in sensing technologies, it's becoming feasible to comfortably measure several aspects of emotions during challenging daily life situations. This work describes how the stress of drivers can be measured through different types of interactions, and how the information can enable several interactions in the car with the goal of helping to manage stress. These new interactions could help not only to bring empathy to the driving experience but also to improve driver safety and increase social awareness.MIT Media Lab ConsortiumNational Science Foundation (U.S.) (Grant No. NSF CCF- 1029585

Towards hybrid driver state monitoring : review, future perspectives and the role of consumer electronics

The purpose of this paper is to bring together multiple literature sources which present innovative methodologies for the assessment of driver state, driving context and performance by means of technology within a vehicle and consumer electronic devices. It also provides an overview of ongoing research and trends in the area of driver state monitoring. As part of this review a model of a hybrid driver state monitoring system is proposed. The model incorporates technology within a vehicle and multiple broughtin devices for enhanced validity and reliability of recorded data. Additionally, the model draws upon requirement of data fusion in order to generate unified driver state indicator(-s) that could be used to modify in-vehicle information and safety systems hence, make them driver state adaptable. Such modification could help to reach optimal driving performance in a particular driving situation. To conclude, we discuss the advantages of integrating hybrid driver state monitoring system into a vehicle and suggest future areas of research

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios

Sobriety Checkpoints: A Constitutional Faux Pas - Michigan Department of State Police v. Sitz

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Prediction of drivers’ performance in highly automated vehicles

Purpose: The aim of this research was to assess the predictability of driver’s response to critical hazards during the transition from automated to manual driving in highly automated vehicles using their physiological data.Method: A driving simulator experiment was conducted to collect drivers’ physiological data before, during and after the transition from automated to manual driving. A total of 33 participants between 20 and 30 years old were recruited. Participants went through a driving scenario under the influence of different non-driving related tasks. The repeated measures approach was used to assess the effect of repeatability on the driver’s physiological data. Statistical and machine learning methods were used to assess the predictability of drivers’ response quality based on their physiological data collected before responding to a critical hazard. Findings: - The results showed that the observed physiological data that was gathered before the transition formed strong indicators of the drivers’ ability to respond successfully to a potential hazard after the transition. In addition, physiological behaviour was influenced by driver’s secondary tasks engagement and correlated with the driver’s subjective measures to the difficulty of the task. The study proposes new quality measures to assess the driver’s response to critical hazards in highly automated driving. Machine learning results showed that response time is predictable using regression methods. In addition, the classification methods were able to classify drivers into low, medium and high-risk groups based on their quality measures values. Research Implications: Proposed models help increase the safety of automated driving systems by providing insights into the drivers’ ability to respond to future critical hazards. More research is required to find the influence of age, drivers’ experience of the automated vehicles and traffic density on the stability of the proposed models. Originality: The main contribution to knowledge of this study is the feasibility of predicting drivers’ ability to respond to critical hazards using the physiological behavioural data collected before the transition from automated to manual driving. With the findings, automation systems could change the transition time based on the driver’s physiological state to allow for the safest transition possible. In addition, it provides an insight into driver’s readiness and therefore, allows the automated system to adopt the correct driving strategy and plan to enhance drivers experience and make the transition phase safer for everyone.</div

Environnement virtuel générateur d’émotions

Les émotions jouent un rôle important dans la prise de décision quotidienne. En effet, elles influencent grandement la manière dont les individus interagissent avec leur environnement. Dans cette étude nous avons premièrement conçu un environnement virtuel de conduite automobile, puis créé des scénarios générateurs d’émotions à l’aide de la méthode Belief-Desire-Intention. Nous avons évalué l’efficacité de ces scénarios à l’aide d’un groupe de 30 personnes et d’un casque électroencéphalogramme pour mesurer leurs émotions. On observe que plus de 70% des scénarios conçus avec cette méthode ont généré l’émotion que l’on avait anticipée chez 52% à 76% des participants. La deuxième phase de cette expérience porte sur la réduction d’émotions avec un agent correcteur. Nous avons noté une efficacité de la réduction des émotions allant de 36.4% jusqu’à 70.0% des participants à travers les différents scénarios.Emotions play an important role in daily decision-making. Indeed, they greatly influence how individuals interact with their environment. In this study, we first designed a virtual driving environment and various emotion-inducing scenarios using the Belief-Desire-Intention method. We evaluated the effectiveness of these scenarios with a group of 30 people and an EEG headset to measure the emotions. Over 70% of scenarios designed with this method induced the emotion that had been anticipated in 52% to 76% of the participants. The second phase of this experiment is the reduction of emotions with a corrective agent. We noted an efficiency in reducing emotions ranging from 36.4% to 70.0% of the participants through the different scenarios

Automotive emotions: a human-centred approach towards the measurement and understanding of drivers' emotions and their triggers

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe automotive industry is facing significant technological and sociological shifts, calling for an improved understanding of driver and passenger behaviours, emotions and needs, and a transformation of the traditional automotive design process. This research takes a human-centred approach to automotive research, investigating the users’ emotional states during automobile driving, with the goal to develop a framework for automotive emotion research, thus enabling the integration of technological advances into the driving environment. A literature review of human emotion and emotion in an automotive context was conducted, followed by three driving studies investigating emotion through Facial-Expression Analysis (FEA): An exploratory study investigated whether emotion elicitation can be applied in driving simulators, and if FEA can detect the emotions triggered. The results allowed confidence in the applicability of emotion elicitation to a lab-based environment to trigger emotional responses, and FEA to detect those. An on-road driving study was conducted in a natural setting to investigate whether natures and frequencies of emotion events could be automatically measured. The possibility of assigning triggers to those was investigated. Overall, 730 emotion events were detected during a total driving time of 440 minutes, and event triggers were assigned to 92% of the emotion events. A similar second on-road study was conducted in a partially controlled setting on a planned road circuit. In 840 minutes, 1947 emotion events were measured, and triggers were successfully assigned to 94% of those. The differences in natures, frequencies and causes of emotions on different road types were investigated. Comparison of emotion events for different roads demonstrated substantial variances of natures, frequencies and triggers of emotions on different road types. The results showed that emotions play a significant role during automobile driving. The possibility of assigning triggers can be used to create a better understanding of causes of emotions in the automotive habitat. Both on-road studies were compared through statistical analysis to investigate influences of the different study settings. Certain conditions (e.g. driving setting, social interaction) showed significant influence on emotions during driving. This research establishes and validates a methodology for the study of emotions and their causes in the driving environment through which systems and factors causing positive and negative emotional effects can be identified. The methodology and results can be applied to design and research processes, allowing the identification of issues and opportunities in current automotive design to address challenges of future automotive design. Suggested future research includes the investigation of a wider variety of road types and situations, testing with different automobiles and the combination of multiple measurement techniques

Perception of Suspense in Live Football Commentaries from German and British Perspectives

Kern F, Trouvain J, Samlowski B. Perception of Suspense in Live Football Commentaries from German and British Perspectives. SpeechProsody 2018-8. 2018