With the development of technology, people's viewpoints of the automobile have shifted; instead of merely a means of transportation, the automobile has become a space in which a driver can still perform daily activities besides driving, such as communicating with other people, interacting with electronic devices, and receiving information. In the meantime, different ways of interaction have been explored. Among all the modalities, gestures have been considered as a feasible way for performing in-car secondary tasks because of their intuitiveness. However, few researches have been conducted in terms of subjects' cognitive load. This thesis has examined four gesture interfaces (air swipe, air tap, screen swipe, and screen tap), in terms of their effects on drivers' driving performance, secondary task performance, perceived cognitive load, and eye glance behavior. The result demonstrated that air gestures are generally slower than screen gestures with regard to secondary performance. Screen swipe gesture requires the lowest cognitive load while air swipe and screen tap gesture remain the same. Subjects in this study tend to prefer screen swipe gesture the most while prefer air tap gesture the least. However, there is no significant difference between air swipe and screen tap gesture. Although air tap gesture and screen tap gesture generated the largest amount of dwell times, no variance among the four gesture interfaces in driving performance has been found. The result indicated that even though air gestures are not limited by space, screen swipe in this study still seemed to be the most ideal way for performing in-car secondary task of music selection.

This research examined the relationship between experience and driving performance with secondary tasks. Data were collected from 42 teenage drivers and their parents using an instrumented vehicle for two one hour test track sessions spaced 12 months apart. For part of the sessions, participants followed a lead vehicle which allowed for range data to be collected. Teenage and experienced drivers' driving were compared for cell phone and odometer tasks. Variables such as Speed, Range to Forward Vehicle, and Driving-Related Eyeglance percentages were all analyzed utilizing ANOVA. Post-hoc analysis on continuous data was performed using a Tukey HSD test. Lane Deviations were examined using Chi-Square analyses. Experienced drivers drove faster overall than teenage drivers. Teenage drivers drove faster in the 12 month session than the first session. No significant effects were found for Speed Variance, Range Variance, or Lane Deviations. Experienced drivers had a higher percentage of driving-related glances than teenage drivers. For the odometer task, teenage drivers were found to follow further behind a lead vehicle than adults. Driving experience was believed to have an effect on driver eyeglance patterns due to increased development of attentional control resulting in better switching between the task and the driving environment. Experienced drivers likely drove faster due to increased confidence in their driving ability. This research supports current GDL cell phone restrictions. A drivers' education lesson plan framework was developed to address these differences. Future research should focus on further refining GDL legislation to address the cognitive differences between teenage and experienced drivers. / Master of Science

<p>Many information technology artefacts can be found in today’s cars. The interaction with these artefacts is the driver’s secondary task while driving the car in a safe way is the primary task. When designing interfaces for in-vehicle usage, measures have to be taken in order to make the interaction with the artefact suit the in-vehicle environment. One of these measures is to have the appropriate screen density level, which is the amount of information present on the screen.</p><p>This thesis compares the usability of two integrated in-vehicle display prototypes, one with low screen density and one with high screen density. The usability comparison considers both safety and user preferences. Safety was measured by a Lane Change Test (LCT) which measures distraction of a primary task while performing a secondary task, and user preferences was measured with a questionnaire. Before the comparison was made, controls and a graphical user interface were designed.</p><p>Results showed no significant difference in driver distraction between performing tasks on the high screen density display and the low screen density display. However, a vast majority of the users preferred high screen density over low. Furthermore, the distraction levels for both the high and the low screen density displays were below the proposed 0.5 meter limit for allowed driver distraction. The results indicate that in-vehicle displays can have a high level of screen density without imposing a level of distraction on the driver that is unsuitable for driving.</p>

Many information technology artefacts can be found in today’s cars. The interaction with these artefacts is the driver’s secondary task while driving the car in a safe way is the primary task. When designing interfaces for in-vehicle usage, measures have to be taken in order to make the interaction with the artefact suit the in-vehicle environment. One of these measures is to have the appropriate screen density level, which is the amount of information present on the screen. This thesis compares the usability of two integrated in-vehicle display prototypes, one with low screen density and one with high screen density. The usability comparison considers both safety and user preferences. Safety was measured by a Lane Change Test (LCT) which measures distraction of a primary task while performing a secondary task, and user preferences was measured with a questionnaire. Before the comparison was made, controls and a graphical user interface were designed. Results showed no significant difference in driver distraction between performing tasks on the high screen density display and the low screen density display. However, a vast majority of the users preferred high screen density over low. Furthermore, the distraction levels for both the high and the low screen density displays were below the proposed 0.5 meter limit for allowed driver distraction. The results indicate that in-vehicle displays can have a high level of screen density without imposing a level of distraction on the driver that is unsuitable for driving.

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.

The overall objective of this thesis was to test the Default Interventionist (DI) account of belief-bias in human reasoning using the novel methodology introduced by Handley, Newstead & Trippas (2011). DI accounts focus on how our prior beliefs are the intuitive output that bias our reasoning process (Evans, 2006), whilst judgments based on logical validity require effortful processing. However, recent research has suggested that reasoning on the basis of beliefs may not be as fast and automatic as previous accounts claim. In order to investigate whether belief based judgments are resource demanding we instructed participants to reason on the basis of both the validity and believability of a conclusion whilst simultaneously engaging in a secondary task (Experiment 1 - 5). We used both a within and between subjects design (Experiment 5) examining both simple and complex arguments (Experiment 4 – 9). We also analysed the effect of incorporating additional instructional conditions (Experiment 7 – 9) and tested the relationships between various individual differences (ID) measures under belief and logic instruction (Experiment 4, 5, 7, 8, & 9). In line with Handley et al.’s findings we found that belief based judgments were more prone to error and that the logical structure of a problem interfered with judging the believability of its conclusion, contrary to the DI account of reasoning. However, logical outputs sometimes took longer to complete and were more affected by random number generation (RNG) (Experiment 5). To reconcile these findings we examined the role of Working Memory (WM) and Inhibition in Experiments 7 – 9 and found, contrary to Experiment 5, belief judgments were more demanding of executive resources and correlated with ID measures of WM and inhibition. Given that belief based judgments resulted in more errors and were more impacted on by the validity of an argument the behavioural data does not fit with the DI account of reasoning. Consequently, we propose that there are two routes to a logical solution and present an alternative Parallel Competitive model to explain the data. We conjecture that when instructed to reason on the basis of belief an automatic logical output completes and provides the reasoner with an intuitive logical cue which requires inhibiting in order for the belief based response to be generated. This creates a Type 1/Type 2 conflict, explaining the impact of logic on belief based judgments. When explicitly instructed to reason logically, it takes deliberate Type 2 processing to arrive at the logical solution. The engagement in Type 2 processing in order to produce an explicit logical output is impacted on by demanding secondary tasks (RNG) and any task that interferes with the integration of premise information (Experiments 8 and 9) leading to increased latencies. However the relatively simple nature of the problems means that accuracy is less affected. We conclude that the type of instructions provided along with the complexity of the problem and the inhibitory demands of the task all play key roles in determining the difficulty and time course of logical and belief based responses.

Driving under distraction may lead drivers to wrong actions that can result in serious accidents. The objective of this thesis was to apply a driving simulator to verify variations in drivers\' behavior while driving. Behavior to drive on a curve was measured by variation in drivers\' speed profile in a virtualized highway. The comparison was performed between two identical simulations, one involving drivers distracted with a mental workload, and other in which they were full aware of driving task. 54 volunteer drivers took part in this study, which was divided into 4 stages. 17 drivers performed the distraction test known as PASAT, and results showed that distracted drivers did not recognize the beginning of the curve and drove through it at speeds higher than those when they were fully aware. Moreover, driving performance was increased when drivers were aware of driving, thereby hitting high speeds in tangents, but perceiving curves in advance to reduce acceleration. This study confirms that driving simulators are beneficial in discovering drivers\' behavior exposed to activities that could be highly risky if driving in real situations. / A distração durante a atividade de direção pode levar o condutor de veículos automotores a cometer falhas, que podem ocasionar até mesmo acidentes graves. Este estudo aborda a utilização de simuladores de direção para verificar variações no comportamento de motoristas ao realizar a atividade de direção, distraídos ou com plena atenção na condução do veículo. O comportamento é medido pela variação no perfil de velocidade dos condutores para desenvolver uma curva considerada perigosa em uma rodovia simulada em ambiente virtual. A variação de velocidade deste perfil é comparada entre duas simulações idênticas, onde em uma delas os condutores estão distraídos com um teste que proporciona estresse mental e, na outra, estão com plena atenção à direção. 54 condutores fizeram parte deste estudo dividido em 3 etapas. 17 participantes realizaram o teste de distração conhecido como PASAT, e a análise dos resultados mostram que, distraídos, os condutores não perceberam o início da curva e desenvolveram velocidades maiores durante seu trajeto. Além disso, quando estavam com plena atenção à atividade de direção, o desempenho dos condutores foi melhor, atingindo velocidades maiores nas tangentes, mas percebendo as curvas antecipadamente e reduzindo suas velocidades antes de iniciar esses trechos.

Driving under distraction may lead drivers to wrong actions that can result in serious accidents. The objective of this thesis was to apply a driving simulator to verify variations in drivers\' behavior while driving. Behavior to drive on a curve was measured by variation in drivers\' speed profile in a virtualized highway. The comparison was performed between two identical simulations, one involving drivers distracted with a mental workload, and other in which they were full aware of driving task. 54 volunteer drivers took part in this study, which was divided into 4 stages. 17 drivers performed the distraction test known as PASAT, and results showed that distracted drivers did not recognize the beginning of the curve and drove through it at speeds higher than those when they were fully aware. Moreover, driving performance was increased when drivers were aware of driving, thereby hitting high speeds in tangents, but perceiving curves in advance to reduce acceleration. This study confirms that driving simulators are beneficial in discovering drivers\' behavior exposed to activities that could be highly risky if driving in real situations. / A distração durante a atividade de direção pode levar o condutor de veículos automotores a cometer falhas, que podem ocasionar até mesmo acidentes graves. Este estudo aborda a utilização de simuladores de direção para verificar variações no comportamento de motoristas ao realizar a atividade de direção, distraídos ou com plena atenção na condução do veículo. O comportamento é medido pela variação no perfil de velocidade dos condutores para desenvolver uma curva considerada perigosa em uma rodovia simulada em ambiente virtual. A variação de velocidade deste perfil é comparada entre duas simulações idênticas, onde em uma delas os condutores estão distraídos com um teste que proporciona estresse mental e, na outra, estão com plena atenção à direção. 54 condutores fizeram parte deste estudo dividido em 3 etapas. 17 participantes realizaram o teste de distração conhecido como PASAT, e a análise dos resultados mostram que, distraídos, os condutores não perceberam o início da curva e desenvolveram velocidades maiores durante seu trajeto. Além disso, quando estavam com plena atenção à atividade de direção, o desempenho dos condutores foi melhor, atingindo velocidades maiores nas tangentes, mas percebendo as curvas antecipadamente e reduzindo suas velocidades antes de iniciar esses trechos.

Remotely-controlled technologies are no longer limited to military applications, such as unmanned military airborne weapons or explosive diffuser robots. Nowadays we can see more and more of remotely controlled devices used as medical equipment, toys, and so forth. One of the most recent areas of interest is robotic telepresence, also known as Mobile Robot Presence (MRP), which provides the ability to interact socially and professionally with other people and even objects in remote locations. One of the known issues with using remotely-controlled devices is the cognitive overload which their operators (pilots) experience and MRP pilots are no exception. However, despite vast research on different ways to address this in military or medical scenarios, little has been done regarding MRPs. This thesis study aims to make a contribution in closing that gap by suggesting a method, developing a prototype implementing it; then conducting an empirical assessment of the method and the prototype as a part of a broader study on MRP, supported by Swedish Research Council. I have suggested a method comprised of a Secondary-task (ST) method and Subjective Rating Scales (SRS), in which the latter act as an evaluation method for the former. Both of them were used in an overarching study in search for the best control device amongst four chosen devices. I collected and analyzed secondary task performance data (e.g. response time, error rates), subjective user ratings, explicit rankings, and observations recordings. My analysis of the collected data shows that using a monitoring and response face recognition secondary task is a plausible method for the assessment of MRP pilot’s cognitive load.

Distracted driving involving secondary tasks is known to lead to an increased likelihood of being involved in motor vehicle crashes. Some secondary tasks are unnecessary and should never be performed. But other secondary tasks, e.g., operating the defroster, are critical to safe driving. Ideally, the driver should schedule when to perform the critical tasks such that the likelihood of a hazard materializing is relatively small during the performance of the secondary task. The current study evaluates a training program -- STRAP (Secondary Task Regulatory & Anticipatory Program) -- which is designed to make drivers aware of latent hazards in the hope that they regulate engagement in secondary tasks which they are performing at the time the latent hazard appears. The secondary tasks include both tasks that require drivers to take their eyes off the road (e.g., operating the defroster) and those which do not (e.g., cell phone use). Participants were assigned either to STRAP or placebo training. After training, the groups navigated eight different scenarios on a driving simulator and were instructed to engage during the drive in as many secondary tasks as possible as long as they felt safe to do so. Secondary task engagement was fully user paced. It is important to note that drivers receiving STRAP training were never instructed directly to either disengage from or not engage in secondary tasks when encountering latent hazards. The results show that STRAP trained drivers were more likely to detect latent hazards and associated clues than placebo trained drivers. With regards to secondary task engagement, STRAP trained drivers chose to limit their in-vehicle and cell phone task engagement by focusing on the forward roadway rather than the task at hand. STRAP training holds out the promise of providing individuals with the necessary skills and proactive awareness to make safe decisions regarding the non-performance or interruption of a secondary task in the presence of a potential latent hazard.