Detection of Discomfort in Autonomous Driving via Stochastic Approximation
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One of the most important goals in the field of autonomous driving development is to make the experience for the passenger as pleasant and comfortable as possible. In addition to traditional influence factors on passenger comfort, new aspects arise due to the transfer of control from the human to the vehicle. Some of these are apparent safety, motion sickness, user preferences regarding driving style and information needs. Ideally, the vehicle and the passenger should form a team, whereby the vehicle should be able to detect and predict situations of discomfort in real time and take measures accordingly. This requires not only the continuous monitoring of the passengers state but also the implementation of adequate mathematical models. To investigate how this teaming of human and automated agents can be shaped in the most effective way is a key topic of the Collaborative Research Center “Hybrid Societies (https://hybrid-societies.org/). In this framework, driving simulator data from the previous project “KomfoPilot” (https://bit.ly/komfopilot) is re-analyzed using new mathematical models. The participants in the study completed several automated drives and reported continuously situations of discomfort using a handset control. Sensor data was collected simultaneously using eye tracking glasses, a smart band, seat pressure sensors and video cameras for motion and face tracking. While pupil diameter, heart rate, interblink intervals, skin conductance and head movement have already been identified as potential single indicators of discomfort, it is now necessary to integrate these and other findings of the project into a functional multivariate model. In this paper, we investigate how such a model can be shaped to offer high prediction accuracy and viable practical implementation. The first important question – which arises from the heterogeneity of the participants – is whether to work with training data on an individual or aggregated level. We compare both possibilities by applying techniques from the field of stochastic approximation for clustering of the chosen training set and subsequent classification of the test data. In the case of an individual model for each participant, we furthermore divide the participants into subgroups and analyze whether there is a connection between the physiological reactions of a passenger and his/her demographic characteristics and driving experience. Finally, we discuss the potential of our method as a reliable prediction model as well as implications for future driving simulator studies and related research.Eye tracking offers researchers an opportunity to collect an objective assessment of visual behavior. Visual behavior—referring broadly to metrics and measures of gaze positioning and movement—has been used to assess variables including exposure time, cognitive processing, prominence, and (visual) attention. Over the past decade, communication science researchers have increased their use of eye-tracking methods in published articles. During that same period, technological innovations have made eye-tracking units more affordable and accessible to interested researchers increasing the likelihood that eye-tracking research will continue to increase in the field. In this article, we provide information on eye tracking in hopes of improving the quality and reporting of eye-tracking research in communication. The article first provides an overview of basic eye-tracking information followed by a systematic review on the reporting of eye-tracking methods in communication-relevant research. We then provide eye-tracking research reporting recommendations and some ideas about how eye tracking might be integrated into scholarly work moving forward with the aim of improving the transparency and replicability of eye-tracking research in communication science.
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This paper discusses an important issue regarding the concept of motion from the perspective of Indian philosophical traditions. We can recognise two camps in Indian philosophical schools regarding the epistemic means (pramana) through which one cognises motion. Some Indian philosophical schools claim that motion is completely imperceptible and one infers motion by perceiving contact and separation of an object with another object or space. Among these schools, we have considered Patanjali and Ramanuja (the author of Tantrarahasya) as the main advocators of this position. The other group claims that motion is perceptible and we infer motion only when the object possessing the motion is not perceptible. Supporters of this position are mainly the Nyaya-Vaisesika school and Narayanabhatta the author of Manameyodaya. While summarising and critically analysing these positions, we support the view that motion is perceptible by showing the following: (1) The position that motion is non-perceptible leads to some ontological issues (2) The position that motion is perceptible is more economical and simpler.
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Traditional motion learning support systems have a single piece of motion data as a model motion although the data is not always the best one for learners. There is also a fear that some learners regard the model motion as the absolute one. To solve this problem, we proposed a system showing various model motions to the learners. They can select one of the following model motion; arbitrary coach's motion, the coach's motion whose physique is the most similar to learner's one, the average motion of all coaches and the nearest motion to the average. On the system, learners are able to choose one from these four types as a model motion if the system has some coaches' motion data. Through an evaluation experiment, we concluded that the system was helpful for motion learning.
Structure from Motion
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Objective: This study aims to examine domestic literature on eye-tracking in the design area, and to present new eye-tracking application directions.BR Background: Eye-tracking was introduced in the experimental psychology field for the first time in the 1950s. Eye-tracking has high utilization values in the design application area because eye-tracking can accumulate data on what people see, providing the quantitative values on eye movement.BR Method: This study examined the papers published in domestic journals, as well as the papers presented in conferences from 2000 to 2016 through DBPIA.BR Results: Although the use of eye-tracking technology was slightly meager in the product design area, it has been actively used for the evaluation analyses of preference and attention in architecture/public design. Eye-tracking also presented a method to design advertisement that is helpful to advertisement effect measurement, and product salesin the advertisement design area. Since detail psychological analysis is possible, the application of eye-tracking in the studies related with user interface has been active.BR Conclusion: The eye-tracking technology is projected to be actively used as a new interface means, such as in helping in disabled people
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The goal of this paper is to explore the benefits of neuromarketing, in general, and the eye tracking method, in particular, in market research, and to consider their ethical implications. In an example of a typical case study I will try to show how eye tracking can be deployed to analyse the unconscious visual perception of a TV commercial by the participants in the study. I analysed a TV commercial for readymade soups, in which five different readymade soups where advertised. The sample contains 21 participants. With the help of eye tracking method in “Gazepoint” software we have discovered that one scene drew attention much more than the others. In addition to this, I review the literature on benefits of eye tracking as a complement to more traditional methods. I also present an overview of ethical issues related with this method.
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An eye-tracking study is presented that investigates how individuals find defects in source code. This work partially replicates a previous eye-tracking study by Uwano et al. [2006]. In the Uwano study, eye movements are used to characterize the performance of individuals in reviewing source code. Their analysis showed that subjects who did not spend enough time initially scanning the code tend to take more time finding defects. The study here follows a similar setup with added eye-tracking measures and analyses on effectiveness and efficiency of finding defects with respect to eye gaze. The subject pool is larger and is comprised of a varied skill level. Results indicate that scanning significantly correlates with defect detection time as well as visual effort on relevant defect lines. Results of the study are compared and contrasted to the Uwano study.
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Eye tracking technology has caught the attention of designers who wish to better understand how users visually interact with products and technologies. Given the availability of eye tracking technologies, which are able to measure users' eye gaze on computer screens, designers and researchers can employ eye tracking to answer a very wide variety of questions about users' visual behavior, cognition, and visual attention strategies. Yet, commercial eye tracking software typically provides a limited number of out-of-the-box analysis tools for understanding the massive amount of data generated during eye tracking sessions. This poster (1) surveys the range of questions that people have successfully asked using eye tracking technology and (2) summarizes how raw eye tracking data is transformed into higher-level constructs that are more appropriate for analysis and the kinds of analysis that can be performed. The goal of this poster is to help designers make more informed decisions when considering the use of eye tracking to aid them in design.
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For decades, researchers from many fields have been utilizing eye tracking as an assessment tool. In human-computer interaction, recent technical advancements indicate eye-gazing as a new input modality. Despite all the improvements and advantages, eye tracking is still seen as challenging and it is not widely used. This research made a preliminary step towards the evaluation of people’s awareness of eye tracking and their attitudes towards it. An online questionnaire was designed, and it is comprised of three users' categories: general users, researchers and eye-tracking researchers. A total of 98 users participated in the survey. The survey indicated that approximately half of the respondents had heard about eye-tracking technology, 47% of them working in the research area. Nevertheless, the majority of them did not know the basic facts and theories about this technology. Even among researchers, only 10% of them had adopted eye tracking, and they agreed on the value eye tracking offers for different research disciplines. nevertheless, they also agreed with the perception that eye tracking is a difficult and expensive research methodology. In conclusion, the results support the usefulness of eye tracking and favorable attitudes towards it among most users, but the low response to the survey could also indicate negative attitudes toward eye tracking and a lack of awareness. Therefore, in order to take advantage of eye-tracking technology, further efforts such as training and cost reduction are recommended to the eye-tracking community and manufacturers.
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