Les études d’opinion se contentent généralement de mesurer une opinion, une réponse considérée comme l’attitude dusujet. Pourtant, si on nous pose la question, nous sommes capables de donner plusieurs opinions, plusieurs réponses que nous avons intériorisées lors de nos interactions avec autrui, dans un répertoire de réponses (Moscovici, 1970). Ce faisant, nous assumons l’attitude d’Autrui (Mead, 1934) qui médiatise ainsi notre rapport à l’objet (Moscovici, 1984) : chaque prise de position est le résultat d’un dialogue interne entretenu par le sujet avec autrui et ses réponses (Marková, 2005). Notre objectif dans cette thèse est d’inventer une méthode permettant d’appréhender ce répertoire de réponses.Nous avons testé ainsi un paradigme qui va nous permettre d’observer, dans nos deux premières études, la multiplicité deréponses dont nous disposons dans notre répertoire, ainsi que leur caractère partagé et cohérent. Une troisième étude, appuyée sur cette méthode, nous a permis de constater que le niveau d’élaboration des répertoires de réponses est lié à l’implication de l’individu vis-à-vis de l’objet. Nous avons également pu observer le développement d’un répertoire de réponses, lors d’une étude de terrain menée, sur deux ans, dans un groupe restreint qui découvrait une nouvelle technologie. De façon générale, cette thèse apporte sa contribution à l’écriture de la grammaire du répertoire de réponses en présentant un paradigme qui souligne le caractère multiple de l’opinion. / Opinion surveys usually settle for measuring opinion, an answer considered to be the participant’s attitude toward the subject. Yet when further questioned, we are able to give several opinions, several answers, that we have internalized from our interactions with others, into a repertoire of responses (Moscovici, 1970). In doing so, we assume the attitude of others (Mead, 1934) thus mediating our relationship to the object (Moscovici, 1984): each position is the result of an internal dialogue, maintained by the subject with others and their responses (Markova, 2005). Our goal in this thesis is to invent a method to understand this repertoire of responses. Thus, in our first two studies, we tested a paradigm that allows us to observe, the multiplicity of responses that are available in our repertoire, as well as their shared and coherent nature. A thirdstudy based on this method, enabled us to observe that the degree of development of the repertoire of responses is related to the involvement of the individual in regard to the object. We were also able to observe the development of a repertoire of responses in a field study lasting over two years, in a small group who discovered a new technology. Generally, this thesis contributes to writing the grammar of the repertoire of responses by presenting a paradigm that emphasizes the varied nature of opinion.

<p>This work explores the possibilities of robust, noise adaptive and automatic segmentation of driver eye movements into comparable quantities as defined in the ISO 15007 and SAE J2396 standards for in-vehicle visual demand measurements. Driver eye movements have many potential applications, from the detection of driver distraction, drowsiness and mental workload, to the optimization of in-vehicle HMIs. This work focuses on SeeingMachines head and eye-tracking system SleepyHead (or FaceLAB), but is applicable to data from other similar eye-tracking systems. A robust and noise adaptive hybrid algorithm, based on two different change detection protocols and facts about eye-physiology, has been developed. The algorithm has been validated against data, video transcribed according to the ISO/SAE standards. This approach was highly successful, revealing correlations in the region of 0.999 between analysis types i.e. video transcription and the analysis developed in this work. Also, a real-time segmentation algorithm, with a unique initialization fefature, has been developed and validated based on the same approach.</p><p>This work enables real-time in-vehicle systems, based on driver eye-movements, to be developed and tested in real driving conditions. Furthermore, it has augmented FaceLAB by providing a tool that can easily be used when analysis of eye movements are of interest e.g. HMI and ergonomics studies, analysis of warnings, driver workload estimation etc.</p>

This work explores the possibilities of robust, noise adaptive and automatic segmentation of driver eye movements into comparable quantities as defined in the ISO 15007 and SAE J2396 standards for in-vehicle visual demand measurements. Driver eye movements have many potential applications, from the detection of driver distraction, drowsiness and mental workload, to the optimization of in-vehicle HMIs. This work focuses on SeeingMachines head and eye-tracking system SleepyHead (or FaceLAB), but is applicable to data from other similar eye-tracking systems. A robust and noise adaptive hybrid algorithm, based on two different change detection protocols and facts about eye-physiology, has been developed. The algorithm has been validated against data, video transcribed according to the ISO/SAE standards. This approach was highly successful, revealing correlations in the region of 0.999 between analysis types i.e. video transcription and the analysis developed in this work. Also, a real-time segmentation algorithm, with a unique initialization fefature, has been developed and validated based on the same approach. This work enables real-time in-vehicle systems, based on driver eye-movements, to be developed and tested in real driving conditions. Furthermore, it has augmented FaceLAB by providing a tool that can easily be used when analysis of eye movements are of interest e.g. HMI and ergonomics studies, analysis of warnings, driver workload estimation etc.

No description available.

L'intérêt philosophique pour la « visibilité » – Sichtbarkeit –, à la fin du XIXe siècle, participe d'une interaction nouvelle entre art, science et technique. Le dessin réunit les arts graphiques comme science du « visible ». Vitruve établit déjà que la « science de l'architecte » – Architecti scientia – prend valeur et se renforce par la « science du dessin » – Graphidis scientia. La recherche porte sur l'analyse du dessin que l'architecte utilise et produit lorsqu'il travaille à s'instruire selon son propre arbitre et ses propres moyens, hors les murs des écoles et des ateliers, au tournant du XIXe au XXe siècle. Charles-Édouard Jeanneret (1887-1965), Paul Tournon (1881-1964) et Jean-Charles Moreux (1889-1956) voyagent pour leur apprentissage. Ils regardent le monde et transcrivent leur étonnement activé au contact de la réalité. De la même manière, pour se former, alors que leur production architecturale sera très différente, ils notent en dessin. Nous interrogeons la réduction intellectuelle et graphique de la note comme une forme courte de représentation. La forme courte est le travail conscient d'analyse et de recherche synthétique. Chaque trait est pensé et pensé comme matérialité. C'est un acte conceptuel d'abstraction. Comment cette forme courte, que nous interprétons dans sa vocation théorique, participe-t-elle de l'acquisition du « regard instruit » de l'architecte ? / The philosophical interest for the « visibility », - Sichtbarkeit -, at the end of the XIXth century, involved a new interaction between art, science and technique. The drawing combines graphic arts as science of « visible ». Vitruve already establishes that the « science of the architect » – Architecti scientia – takes value and is reinforced by the « science of the drawing » – Graphidis scientia. The research relates to the analysis of the drawing which the architect uses and produces when he works to educate himself, according to his own referee and his own means, out of schools and the workshops, in the bend of the XIXth in the XXth century. Charles-Edouard Jeanneret (1887-1965), Paul Tournon (1881-1964) and Jean-Charles Moreux (1889-1956) travel for their learning. They look at the world and transcribe their astonishment activated in contact with reality. In the same way to form, while their architectural production will be very different, they note in drawing. We interview the intellectual and graphical reduction of note drawing, as a short form of representation. The short form is the conscious work of analysis and of synthetic research. Each feature is thought and thought like materiality. It is a conceptual act of abstraction. How this short form, that we interpret in his theoretical vocation, takes part-such of the acquisition of the architect's trained glance ?

Syftet med denna uppsats var att undersöka valet och upplevelsen av tåg som färdmedel vid en semesterresa. Intresset för tågsemester har ökat markant de senaste åren, därför ämnade studien få en större förståelse för tågtransport. Studien var kvalitativ, sju resenärer i åldern 50–80 år som under de senaste åren genomfört en tågsemester intervjuades. Genomgående resonemang och motiv bakom valet av tåg som färdmedel var dels en miljömedvetenhet men också ett sökande efter den resemodellen och det äventyret långväga tågtransport ger. Transporten var således av experientiell karaktär, det fanns ett värde i själva i transporten. Vidare möjliggjorde tågtransporten möten med både människor och landskap, något som uppskattades av resenärerna. Landskapet upplevdes genom ”travel glance”, den filmiska vyn en immobil passagerare får i ett mobilt färdmedel. Travel glance, resvägens klimatförändringar och ett stort transportansvar gjorde att tågtransporten kändes påtaglig med följden att tåget upplevdes som en plats med lokala anspelningar. Den största skillnaden till andra färdmedel var skillnaden till flyg, tåget upplevdes mer som en plats medan flygplanet mer som en icke-plats. Flygplanet har färre lokala anspelningar. Att tåget var en plats var något som resenärerna värdesatte. Upplevelsen av tåg som färdmedel var positiv, tågtransporten tillförde värde till semesterns helhetsupplevelse.

Driving field experiment data were used to investigate lane changes in which a slow lead vehicle was present to: 1) characterize lane changes, 2) develop predictive models, 3) provide collision avoidance system (CAS) design guidelines. A total of 3,227 slow lead vehicle lane changes over 23,949 miles were completed by sixteen commuters. Two instrumented vehicles, a sedan and an SUV, were outfitted with video, sensor, and radar data systems that collected data in an unobtrusive manner. Results indicate that 37.2% of lane changes are slow lead vehicle lane changes, with a mean completion time of 6.3 s; most slow lead vehicle lane changes are leftward, rated low in urgency and severity. A stratified sample of 120 lane changes was selected to include a range of maneuvers. On the interstate, lane changes are performed less often, <i>t</i>(30) = 2.83, <i>p</i> = 0.008, with lower urgency ratings, <i>F</i>(1, 31) = 5.24, <i>p</i> = 0.05, as compared to highway lane changes, as interstates are designed for smooth flow. Drivers who usually drive sedans are more likely to make lane changes than drivers of SUVs, <i>X</i> ²⁺(1)= 99.6247, <i>p</i> < 0.0001, suggesting that driving style is maintained regardless of which experimental vehicle is driven. Turn signals are used 64% of the time but some drivers signal after the lane change starts. Of cases in which signals are not used, 70% of them are made with other vehicles nearby. Eyeglance analysis revealed that the forward view, rearview mirror, and left mirror are the most likely glance locations. There are also distinct eyeglance patterns for lane changing and baseline driving. Recommendations are to use forward view or mirror-based visual displays to indicate presence detection, and auditory displays for imminent warnings. The "vehicle + signal" logistic regression model is best overall since it takes advantage of the distance to the front and rear adjacent vehicle, forward time-to-collision (TTC), and turn signal activation. The use of additional regressors would also improve the model. Five design guidelines are included to aid in the development of CAS that are useable, safe, and integrated with other systems, given testing and development. / Ph. D.

Increasingly, many of today’s vehicles offer Society of Automotive Engineers (SAE) partially automated driving (PAD) and a limited number of SAE conditionally automated vehicles (CAD) are being developed. Vehicles with PAD systems support the driver through longitudinal and lateral control inputs. However, during PAD the driver must be prepared to take control of the vehicle at any time, requiring them to monitor the environment and PAD system. In contrast, during CAD the driver is not required to monitor the environment or system but must take control when prompted by the system. Given the ability of CAD vehicles to operate in PAD and manual driving, it is important to consider drivers’ mode awareness, that is, their ability to follow the state of the automated system and predict the implications of this status for vehicle control and monitoring responsibilities. In addition, since CAD does not require drivers to keep their visual or attentional resources on the driving task or environment, drivers are allowed to perform secondary tasks (i.e., non-driving related tasks (NDRTs)). Given that drivers will freely choose what types of tasks they do during CAD it is important to build an understanding of whether drivers will choose to engage in NDRTs in the CAD state, and drivers’ ability to perform NDRTs during CAD. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in NDRTs, and their ability to perform scheduled smartphone NDRTs, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method to simulate a vehicle capable of Assisted Driving (similar to PAD) and Automated Driving (similar to CAD). A total of 36 drivers completed the on-road experiment, and experienced stable periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ mode awareness was measured. Drivers’ performance of NDRTs and behavioral adaptation during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in unscripted NDRTs during automated driving, participants were allowed to freely choose to engage in smartphone NDRTs between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results from a mixed-model ANOVA showed that participants’ mode awareness of their role in Automated Driving statistically significantly increased from the first activation to the subsequent activations. There was no statistically significant effect of activation period on drivers’ willingness to engage in NDRTs, as measured by the mean percentage of time drivers chose to engage in NDRTs during Automated Driving, or driver’s ability to perform tasks, as measured by the mean task completion time of the experimenter administered smartphone NDRTs. The mean number of participants who chose to engage in an NDRT (73.8%) and the percentage of time spent on NDRTs per Automated Driving activation period (M=20.37%; SD=23.9), indicated that drivers were willing to engage in NDRTs during Automated Driving. In addition, drivers showed a high level of task performance, completing 95% of the scheduled NDRTs correctly. Altogether, these results suggest that drivers are willing to engage in and perform NDRTs during Automated Driving and that driver behavior during Automated Driving is consistent and stable during a two-hour exposure period. Finally, the findings indicate that requiring the participant to control the vehicle manually for a brief period prior to transitioning to a level of automation that allows the driver to take their visual and attentional resources away from the roadway environment results in statistically significantly less NDRT engagement compared to when participants transition directly to this level of automation. Overall, the findings from this study have methodological and potential system design implications that can help guide the future research on and design of automated driving systems. / M.S. / Increasingly, many of today’s vehicles offer automated driving technology (i.e., Assisted Driving) that support the driver through steering, braking, and accelerating the vehicle. However, during this level of automation the driver must be prepared to take control of the vehicle, requiring them to monitor the environment and the automated driving system. In addition, a limited number of vehicles offer automated driving technology (i.e., Automated Driving) that controls the vehicle and does not require the driver to monitor the environment or system, however, the driver must take control when prompted by the system. Vehicles capable of Automated Driving can also operate in Assisted and manual driving modes. Given the ability of Automated Driving vehicles to operate in Assisted and manual driving, it is important to consider driver’s ability to follow and predict the behavior of the automated system. In addition, since Automated Driving does not require drivers to keep their eyes or mind on driving or monitoring the road, drivers are allowed to perform secondary tasks. Since drivers are free to choose what types of tasks they do during Automated Driving, it is important to understand whether drivers will choose to engage in Secondary tasks, and their ability to perform these tasks during Automated Driving. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in tasks, and their ability to perform scheduled smartphone tasks, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method. The WoZ method uses a concealed human to simulate an automated computer system, in this case an automated driving system. A total of 36 drivers completed the on-road experiment. The participants experienced periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ knowledge of who/what was controlling the vehicle and the driver’s role in the current automated mode was measured. Drivers’ performance of tasks during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in tasks during automated driving, participants were allowed to freely choose to engage in smartphone tasks between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results showed that participants’ ability to identify their role in Automated Driving increased from the first time they experienced the system to the subsequent times. There was no change in drivers’ willingness to engage in tasks or drivers’ ability to perform tasks as they gained more experience with the Automated Driving system. However, the level of task engagement indicated that drivers were immediately willing to engage in tasks during Automated Driving. Drivers also showed a high-level of task performance. Taken together, these findings indicate that drivers are willing to engage in and perform non-driving related tasks during Automated Driving. These findings can help guide future research focused on automated systems and the design of automated driving systems.

Drivers require a steady stream of relevant but focused visual input to make decisions. Most driving information comes from the surrounding environment so keeping drivers' eyes on the road is paramount. However, important information still comes from in-vehicle displays. With this in mind, there has been renewed recent interest in delivering driving in-formation via head-up display. A head-up display (HUD) can present an image directly on-to the windshield of a vehicle, providing a relatively seamless transition between the display image and the road ahead. Most importantly, HUD use keeps drivers' eyes focused in the direction of the road ahead. The transparent display coupled with a new location make it likely that HUDs provide a fundamentally different driving experience and may change the way people drive, in both good and bad ways. Therefore, the objectives of this work were to 1) understand changes in drivers' glance behaviors when using different types of displays, 2) investigate the impact of HUD position on glance behaviors, and 3) examine the impact of HUD graphic type on drivers' behaviors. Specifically, we captured empirical data regarding changes in driving behaviors, glance behaviors, reported workload, and preferences while driving performing a secondary task using in-vehicle displays. We found that participants exhibited different glance behaviors when using different display types, with participants allocating more and longer glances towards a HUD as compared to a traditional Head-Down Display. However, driving behaviors were not largely affected and participants reported lower workload when using the HUD. HUD location did not cause large changes in glance behaviors, but some driving behaviors were affected. When exam-ining the impact of graphic types on participants, we employed a novel technique for ana-lyzing glance behaviors by dividing the display into three different areas of interest relative to the HUD graphic. This method allowed us to differentiate between graphic types and to better understand differences found in driving behaviors and participant preferences than could be determined with frequently used glance analysis methods. Graphics that were fixed in place rather than animated generally resulted in less time allocated to looking at the graphics, and these changes were likely because the fixed graphics were simple and easy to understand. Ultimately, glance and driving behaviors were affected at some level by the display type, display location, and graphic type as well as individual differences like gender and age. / Ph. D. / Drivers gather most of the information that they need to drive by looking at the world around them and at displays within the vehicle. However, research has shown that looking down at vehicle displays can be distracting to drivers which could be unsafe. Therefore, automotive manufacturers look for new ways to help decrease driver distraction, and one potential solution to this problem is the introduction of head-up displays (HUDs). By displaying a graphic on a see-through surface, like a windshield, we can add information to the world in front of the driver. This means that drivers no longer have to physically look away from the road to gather information, and they may be able to use peripheral vision to help drive while they look at the display. While the technology is promising, it is important that we fully understand other impacts of this technology on drivers before we widely incorporate it into vehicles. Therefore, the purpose of this work is to understand how HUDs change drivers’ ability to drive and their glance patterns as they gather the visual information needed to drive safely. We examined differences between HUDs and traditional displays found in vehicles. We then gathered data regarding the location of HUDs. Finally, we tested different graphics displayed on the HUD. In addition to gathering data about glance and driving behaviors, we also gathered data about drivers’ preferences and experiences with the displays. HUDs may tempt drivers to look away from the road for longer periods of time without negatively affecting their driving behaviors. Different HUD locations did not cause large differences in glance behaviors but did have some impact on driving behaviors. Finally, different graphics resulted in very different glance behaviors without significantly changing driving behaviors. These results suggest that HUDs may capture drivers’ attention and cause drivers to be less observant of other elements around them as they drive. However, because different graphics result in different glance patterns, with careful design we may be able to help drivers keep their eyes on the road while safely gathering necessary information from the vehicle.

No description available.