Automatic detection of a driver’s complex mental states

Automatic classification of drivers’ mental states is an important yet relatively unexplored topic. In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We present our video segmentation and annotation methodology of a spontaneous dataset of natural driving videos from 10 different drivers. We also present our real-time annotation tool used for labelling the dataset via an emotion perception experiment and discuss the challenges faced in obtaining the ground truth labels. Finally, we present a methodology for automatic classification of drivers’ mental states. We compare SVM models trained on our dataset with an existing nearest neighbour model pre-trained on posed dataset, using facial Action Units as input features. We demonstrate that our temporal SVM approach yields better results. The dataset’s extracted features and validated emotion labels, together with the annotation tool, will be made available to the research community

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

What Makes Delusions Pathological?

Bortolotti argues that we cannot distinguish delusions from other irrational beliefs in virtue of their epistemic features alone. Although her arguments are convincing, her analysis leaves an important question unanswered: What makes delusions pathological? In this paper I set out to answer this question by arguing that the pathological character of delusions arises from an executive dysfunction in a subject’s ability to detect relevance in the environment. I further suggest that this dysfunction derives from an underlying emotional imbalance—one that leads delusional subjects to regard some contextual elements as deeply puzzling or highly significant

Designing Auditory Warning Signals to Improve the Safety of Commercial Vehicles

Based on four studies, this thesis aims to explore how to design auditory warning signals that can facilitate safer driving by operators of heavy goods vehicles. The first three studies focus on the relationships between certain characteristics of auditory warnings and various indicators of traffic safety. A deeper understanding of these relationships would allow system developers to design auditory signals that are better optimised for safety. The fourth study examines the opinions of both vehicle developers and professional drivers regarding warning attributes. One major conclusion is that meaningful warning sounds that are related to the critical event can improve safety. As compared with arbitrarily mapped sounds, meaningful sounds are easier to learn, can improve drivers’ situation awareness, and generate less interference and less annoyance. The present thesis also supports the view that commercial drivers’ initial acceptance of these sounds may be very high. Annoyance is an especially important aspect of warning design to consider; it can negatively influence driving performance and may lead drivers to turn off their warning systems. This research supports the notion that drivers do not consider that negative experience is an appropriate attribute of auditory warnings designed to increase their situation awareness. Also, commercial drivers seem to report, significantly more than vehicle developers, that having less-annoying auditory warnings is important in high-urgency driving situations. Furthermore, the studies presented in this thesis indicate that annoyance cannot be predicted based on the physical properties of the warning alone. Learned meaning, appropriateness of the mapping between a warning and a critical event, and individual differences between drivers may also significantly influence levels of annoyance. Arousal has been identified as an important component of driver reactions to auditory warnings. However, high levels of arousal can lead to a narrowing of attention, which would be suboptimal for critical situations during which drivers need to focus on several ongoing traffic events. The present work supports the notion that high-urgency warnings can influence commercial drivers’ responses to unexpected peripheral events (i.e., those that are unrelated to the warning) in terms of response force, but not necessarily in terms of response time. The types of auditory warnings that will be developed for future vehicles depend not only on advances in research, but also on the opinions of developers and drivers. The present research shows that both vehicle developers and drivers are aware of several of the potentially important characteristics of auditory warnings. For example, they both recognise that warnings should be easy to understand. However, they do disagree regarding certain attributes of warnings, and, furthermore, developers may tend to employ a “better safe than sorry” strategy (by neglecting factors concerning annoyance and the elicitation of severe startled responses) when designing high-urgency warnings. Developers’ recognition of the potentially important attributes of auditory warnings should positively influence the future development of in-vehicle systems. However, considering the current state of research regarding in-vehicle warnings, it remains challenging to predict the most suitable sounds for specific warning functions. One recommendation is to develop a design process that examines the appropriateness of in-vehicle auditory warnings. This thesis suggests an initial version of such a process, which in this case was produced in collaboration with system designers working in the automotive industry

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Neuroeconomics: How Neuroscience Can Inform Economics

Neuroeconomics uses knowledge about brain mechanisms to inform economic analysis, and roots economics in biology. It opens up the "black box" of the brain, much as organizational economics adds detail to the theory of the firm. Neuroscientists use many tools— including brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems ("executive function") interrupt automatic ones. Emotions and cognition both guide decisions. Just as prices and allocations emerge from the interaction of two processes—supply and demand— individual decisions can be modeled as the result of two (or more) processes interacting. Indeed, "dual-process" models of this sort are better rooted in neuroscientific fact, and more empirically accurate, than single-process models (such as utility-maximization). We discuss how brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state-dependence. We also discuss applications to intertemporal choice, risk and decision making, and game theory. Intertemporal choice appears to be domain-specific and heavily influenced by emotion. The simplified ß-d of quasi-hyperbolic discounting is supported by activation in distinct regions of limbic and cortical systems. In risky decision, imaging data tentatively support the idea that gains and losses are coded separately, and that ambiguity is distinct from risk, because it activates fear and discomfort regions. (Ironically, lesion patients who do not receive fear signals in prefrontal cortex are "rationally" neutral toward ambiguity.) Game theory studies show the effect of brain regions implicated in "theory of mind", correlates of strategic skill, and effects of hormones and other biological variables. Finally, economics can contribute to neuroscience because simple rational-choice models are useful for understanding highly-evolved behavior like motor actions that earn rewards, and Bayesian integration of sensorimotor information

Exploring emotion responses toward pedestrian crossing actions for designing in-vehicle empathic interfaces

While affective non-verbal communication between pedestrians and drivers has been shown to improve on-road safety and driving experiences, it remains a challenge to design driver assistance systems that can automatically capture these affective cues. In this early work, we identify users' emotional self-report responses towards commonly occurring pedestrian actions while crossing a road. We conducted a crowd-sourced web-based survey (N=91), where respondents with prior driving experience viewed videos of 25 pedestrian interaction scenarios selected from the JAAD (Joint Attention for Autonomous Driving) dataset, and thereafter provided valence and arousal self-reports. We found participants' emotion self-reports (especially valence) are strongly influenced by actions including hand waving, nodding, impolite hand gestures, and inattentive pedestrian(s) crossing while engaged with a phone. Our findings provide a first step towards designing in-vehicle empathic interfaces that can assist in driver emotion regulation during on-road interactions, where the identified pedestrian actions serve as future driver emotion induction stimuli