<p>Adults aged 65 years and older have become the fastest-growing age group worldwide and are known to face perceptual, cognitive, and physical challenges in later stages of life. Automation may help to support these various age-related declines. However, many current automated systems often suffer from design limitations and occasionally require human intervention. To date, there is little guidance on how to design human-machine interfaces (HMIs) to help a wide range of users, especially older adults, transition to manual control. Multimodal interfaces, which present information in the visual, auditory, and/or tactile sensory channels, may be one viable option to communicate roles in human-automation systems, but insufficient empirical evidence is available for this approach. Also, the aging process is not homogenous across individuals, and physical and cognitive factors may better indicate one’s aging trajectory. Yet, the benefits that such individual differences have on task performance in human-automation systems are not well understood. Thus, the purpose of this dissertation work was to examine the effects of 1) multimodal interfaces and 2) one particular non-chronological age factor, engagement in physical exercise, on transitioning from automated to manual control dynamic automated environments. Automated driving was used as the testbed. The work was completed in three phases. </p><p><br></p> <p>The vehicle takeover process involves 1) the perception of takeover requests (TORs), 2) action selection from possible maneuvers that can be performed in response to the TOR, and 3) the execution of selected actions. The first phase focused on differences in the detection of multimodal TORs between younger and older drivers during the initial phase of the vehicle takeover process. Participants were asked to notice and respond to uni-, bi- and trimodal combinations of visual, auditory, and tactile TORs. Dependent measures were brake response time and maximum brake force. Overall, bi- and trimodal warnings were associated with faster responses for both age groups across driving conditions, but was more pronounced for older adults. Also, engaging in physical exercise was found to be correlated with smaller maximum brake force. </p><p><br></p> <p>The second phase aimed to quantify the effects of age and physical exercise on takeover task performance as a function of modality type and lead time (i.e., the amount of time given to make decisions about which action to employ). However, due to COVID-19 restrictions, the study could not be completed, thus only pilot data was collected. Dependent measures included decision making time and maximum resulting jerk. Preliminary results indicated that older adults had a higher maximum resulting jerk compared to younger adults. However, the differences in decision-making time and maximum resulting jerk were narrower for the exercise group (compared to the non-exercise group) between the two age groups. </p><p><br></p> <p>Given COVID-19 restrictions, the objective of phase two shifted to focus on other (non-age-related) gaps in the multimodal literature. Specifically, the new phase examined the effects of signal direction, lead time, and modality on takeover performance. Dependent measures included pre-takeover metrics, e.g., takeover and information processing time, as well as a host of post-takeover variables, i.e., maximum resulting acceleration. Takeover requests with a tactile component were associated with the faster takeover and information processing times. The shorter lead time was correlated with poorer takeover quality.</p><p><br></p> <p>The third, and final, phase used knowledge from phases one and two to investigate the effectiveness of meaningful tactile signal patterns to improve takeover performance. Structured and graded tactile signal patterns were embedded into the vehicle’s seat pan and back. Dependent measures were response and information processing times, and maximum resulting acceleration. Overall, in only instructional signal group, meaningful tactile patterns (either in the seat back or seat pan) had worse takeover performance in terms of response time and maximum resulting acceleration compared to signals without patterns. Additionally, tactile information presented in the seat back was perceived as most useful and satisfying.</p><p><br></p> <p>Findings from this research can inform the development of next-generation HMIs that account for differences in various demographic factors, as well as advance our knowledge of the aging process. In addition, this work may contribute to improved safety across many complex domains that contain different types and forms of automation, such as aviation, manufacturing, and healthcare.</p>

No description available.

Through a history of more than 50 years, the results of mathematical models have shown that controller workload is being driven by the complexity involved in the airspace environment. Part of this complexity is prompted by the dynamical behavior of traffic patterns. From the results of models describing controller’s workload, it is observed that predictability decreases the complexity. Therefore, the general idea behind this topic is to analyze how a specific notion of predictability influences the controller’s workload. This specific notion in this research is a type of automation that aircraft benefit from. In a more specific sense, the goal of this research was to analyze how the controllers handle the air traffic in different complex situations when exposed to different automation levels. The following dilemmas are focused through this work: - Information visualization of controllers’ interaction with radar screen - Quantification of dynamics of air traffic patterns - Modeling and quantification of controllers’ workload First, in order to have a grasp of the controllers’ interaction with the air traffic patterns, the controllers’ activities on the radar screen have been visualized in chapter 2. The visualization results for different automated conditions have been analyzed. Based on such analysis the criteria for problem space has been addressed and the main research question is identified. Next in chapter 3, the airspace complexity caused by air traffic flow has been studied and a set of known complexity factors are quantified using a novel calculation approach. With a logistics perspective toward airspace complexity, to calculate each complexity factor, a mathematical formulation has been used and the effects of each corresponding factor on controllers’ workload are addressed. Then in chapter 4, a novel approach toward modeling controller’s workload is presented. After implementing the model on 18 different scenarios, a model for controller’s workload has been developed in which around 60 percent of the en-route air traffic complexity values and around 80 percent of terminal air traffic complexity values could be well-matched with the workload values. From statistical point of view, the results are very much acceptable for experiments in which human factors are involved. Cognitive load has not been considered in the workload model which is the focus of a future work. Later on in chapter 5, the results for each complexity factor as well as workload models are analyzed and discussed for each sector separately. Based on the airspace complexity results, areas where traffic situation had become complex were identified and the controller’s response to different situations are discussed. For each complexity factor as well as workload, the results for three different scenarios featuring different automation levels for two en-route and terminal sectors are compared. At last in chapter 6, the main ideas are discussed, thesis conclusions are presented and possible future work is suggested.

Systeme zum automatisierten Fahren erlauben es, die Fahrzeugführung in einem gewissen Maß vom Fahrer an das Fahrzeug zu übertragen. Da der Fahrer auf diese Weise unterstützt, entlastet oder sogar ersetzt werden kann, werden Systeme zum automatisierten Fahren mit einem großen Potential für Verbesserungen hinsichtlich Straßenverkehrssicherheit, Fahrkomfort und Effizienz verbunden - vorausgesetzt, dass diese Systeme angemessen benutzt werden. Systeme zum hochautomatisierten Fahren stellen in diesem Zusammenhang eine besondere Herausforderung für die Mensch-Maschine-Interaktion dar: So wird es dem Fahrer bei diesem Automatisierungsgrad zwar zum ersten mal ermöglicht, das System nicht mehr permanent überwachen zu müssen und somit die Fahrtzeit potentiell für fahrfremde Tätigkeiten zu nutzen. Es wird jedoch immer noch erwartet, dass der Fahrer nach einer vorherigen angemessenen Übernahmeaufforderung die Fahrzeugführung im Bedarfsfall gewährleisten kann. Angemessenes Automatisierungsvertrauen stellt daher eine zentrale Komponente für die erfolgreiche Kooperation zwischen Fahrern und Systemen zum hochautomatisierten Fahren dar und sollte bei der Gestaltung derartiger Systeme berücksichtigt werden. Frühere Befunde weisen beispielsweise bereits darauf hin, dass unterschiedliche Informationen über automatisierte Systeme ein möglicher Ansatz sein könnten um das Automatisierungsvertrauen des Fahrers aktiv zu gestalten. Automatisierungsvertrauen als Variable in der Gestaltung von Fahrzeugtechnologie zu berücksichtigen erfordert jedoch zunächst auch in der Lage zu sein, Automatisierungsvertrauen adäquat messen zu können. In diesem Sinne war die Zielsetzung dieser Arbeit einerseits die Untersuchung verschiedener Methoden zur Messung des Automatisierungsvertrauens des Fahrers sowie andererseits die Identifikation, prototypische Umsetzung und Bewertung potentieller Ansätze zur Gestaltung von Automatisierungsvertrauen im Kontext von Systemen zum hochautomatisierten Fahren. Zu diesem Zweck wurden drei Fahrsimulatorstudien mit insgesamt N = 280 Probanden durchgeführt. Die vorliegenden Ergebnisse weisen darauf hin, dass (i) sowohl Selbstberichtsverfahren als auch Verhaltensmaße prinzipiell dazu verwendet werden können um das Automatisierungsvertrauen des Fahrers in Systeme zum hochautomatisierten Fahren zu operationalisieren, (ii) eine vorherige Auseinandersetzung mit funktionalen Grenzen von Systemen zum hochautomatisierten Fahren einen nachhaltigen Effekt auf das Automatisierungsvertrauen des Fahrers in das System haben kann und (iii) insbesondere Informationen über die Funktionsweise von Systemen zum hochautomatisierten Fahren das Automatisierungsvertrauen des Fahrers in derartige Systeme verbessern können. Damit liefert die vorliegende Arbeit sowohl wertvolle Ansatze zur Messbarmachung als auch Hinweise für die Gestaltung von Automatisierungsvertrauen im Kontext des hochautomatisierten Fahrens. Darüber hinaus können die Befunde dieser Arbeit in gewissem Maße auch auf andere Arten von Fahrzeugautomatisierung sowie unterschiedliche Domänen und Anwendungen von Automatisierung übertragen werden. / Automated driving systems allow to transfer a certain degree of vehicle control from the driver to a vehicle. By assisting, augmenting or even supplementing the driver, automated driving systems have been associated with enormous potential for improving driving safety, comfort, and efficiency - provided that they are used appropriately. Among those systems, conditional automated driving systems are particularly challenging for human-automation interaction: While the driver is no longer required to permanently monitor conditional automated driving systems, he / she is still expected to provide fallback performance of the dynamic driving task after adequate prior notification. Therefore, facilitating appropriate automation trust is a key component for enabling successful cooperation between drivers and conditional automated driving systems. Earlier work indicates that providing drivers with proper information about conditional automated driving systems might be one promising approach to do this. Considering the role of automation trust as a variable in the design of vehicle technology, however, also requires that drivers` automation trust can be viably measured in the first place. Accordingly, the objectives of this thesis were to explore difffferent methods for measuring drivers` automation trust in the context of conditional automated driving as well as the identification, implementation and evaluation of possible approaches for designing drivers` automation trust in conditional automated driving systems. For these purposes, three driving simulator studies with N = 280 participants were conducted. The results indicate that (i) both self-report measures and behavioral measures can be used to assess drivers` automation trust in conditional automated driving systems, (ii) prior familiarization with system limitations can have a lasting effffect on drivers` automation trust in conditional automated driving systems and (iii) particularly information about the processes of conditional automated driving systems might promote drivers` automation trust in these systems. Thus, the present research contributes much needed approaches to both measuring and designing automation trust in the context of conditional automated driving. In addition, the current findings might also be transferred to higher levels of driving automation as well as other domains and applications of automation.

Although most modern, highly-computerized flight decks are known to be robust to small disturbances and failures, humans still play a crucial role in advanced decision making in off-nominal situations, and accidents still occur because of poor human-automation interaction. In addition to the physical state of the environment, operators now have to extend their awareness to the state of the automated flight systems. To guarantee the accuracy of this knowledge, humans need to know the dynamics or approximate versions of the dynamics that rule the automation. The operator's situation awareness can decline because of a deficient mental model of the aircraft and an excessive workload. This work describes the creation of a computational human agent model simulating cognitive constructs such as situation awareness and mental models known to capture the symptoms of poor human-automation interaction and provide insight into more comprehensive metrics supporting the validation of automated systems in aviation.

[pt] Hoje, os constantes avanços tecnológicos em interfaces digitais, e por consequência as interfaces gráficas do usuário, se fazem cada vez mais presentes na interação humano-máquina. Porém, num contexto em que sistemas inteligentes, a exemplo dos sistemas robóticos, já são uma realidade, ainda restam lacunas a serem preenchidas quando se pensa em integrar, com fluidez, robôs a trabalhos customizados e complexos. Esta pesquisa tem como foco a análise da usabilidade de interfaces de usuário específicas para a interação com robôs remotos também conhecidas como Robot User Interfaces (RUIs). Quando bem executadas, tais interfaces permitem aos operadores realizar remotamente tarefas em ambientes complexos. Para tanto, trabalha-se com a hipótese de que, se RUIs forem concebidas considerando as especificidades desses modelos de interação, as falhas operacionais serão reduzidas. O objetivo desta pesquisa foi avaliar diretrizes específicas para sistemas robóticos, compreendendo a relevância destas na usabilidade de interfaces. Para uma base teórica, foram levantados os modelos já existentes de interação com robôs e sistemas automatizados; e os princípios de design que se aplicam a estes modelos. Após a revisão bibliográfica, foram realizadas entrevistas contextuais com usuários de sistemas robóticos e testes de usabilidade, a fim de reproduzir, em interfaces com e sem diretrizes de RUIs, os processos de interação na realização de tarefas. Os resultados finais das técnicas aplicadas apontaram para a validade da hipótese - se interfaces específicas para sistemas robóticos forem concebidas considerando as especificidades dos modelos de interação humano-robô, as falhas operacionais na interação serão reduzidas - à medida que os sistemas desenvolvidos com interfaces específicas ao contexto de interação com robôs proporcionaram uma melhor usabilidade e mitigaram a ocorrência de uma série de possíveis falhas humanas. / [en] Nowadays, the constant technological advances, and consequently the graphical user interfaces, have become more and more present in the humanmachine interaction. However, in a context where intelligent systems, such as robotic systems, are already a reality, there are still gaps to be filled when we think about integrating robots with custom and complex activities. This research aims on the analysis of Robot User Interfaces (RUIs) usability. When well executed, such interfaces allow operators to remotely perform tasks in complex environments. To that intent, our hypothesis is that, if RUIs are conceived considering the specificities of these interaction models, operational failures will be reduced. The main goal of this research was to evaluate specific guidelines for robotic systems, understanding their relevance in usability. For a theoretical basis, the existing models of interaction with robots and autonomous systems were raised; as well as the design principles that apply to these models. After a bibliographic review, we conducted contextual interviews with users of robotic systems, and usability tests to reproduce, in interfaces with and without RUI guidelines, the interaction processes in the task completion. The final results of the applied techniques proved the validity of the hypothesis, as the systems developed with interfaces specific to the interaction with robots provided better usability and mitigated the occurrence of array of human faults.

Systeme zum automatisierten Fahren erlauben es, die Fahrzeugführung in einem gewissen Maß vom Fahrer an das Fahrzeug zu übertragen. Da der Fahrer auf diese Weise unterstützt, entlastet oder sogar ersetzt werden kann, werden Systeme zum automatisierten Fahren mit einem großen Potential für Verbesserungen hinsichtlich Straßenverkehrssicherheit, Fahrkomfort und Effizienz verbunden - vorausgesetzt, dass diese Systeme angemessen benutzt werden. Systeme zum hochautomatisierten Fahren stellen in diesem Zusammenhang eine besondere Herausforderung für die Mensch-Maschine-Interaktion dar: So wird es dem Fahrer bei diesem Automatisierungsgrad zwar zum ersten mal ermöglicht, das System nicht mehr permanent überwachen zu müssen und somit die Fahrtzeit potentiell für fahrfremde Tätigkeiten zu nutzen. Es wird jedoch immer noch erwartet, dass der Fahrer nach einer vorherigen angemessenen Übernahmeaufforderung die Fahrzeugführung im Bedarfsfall gewährleisten kann. Angemessenes Automatisierungsvertrauen stellt daher eine zentrale Komponente für die erfolgreiche Kooperation zwischen Fahrern und Systemen zum hochautomatisierten Fahren dar und sollte bei der Gestaltung derartiger Systeme berücksichtigt werden. Frühere Befunde weisen beispielsweise bereits darauf hin, dass unterschiedliche Informationen über automatisierte Systeme ein möglicher Ansatz sein könnten um das Automatisierungsvertrauen des Fahrers aktiv zu gestalten. Automatisierungsvertrauen als Variable in der Gestaltung von Fahrzeugtechnologie zu berücksichtigen erfordert jedoch zunächst auch in der Lage zu sein, Automatisierungsvertrauen adäquat messen zu können. In diesem Sinne war die Zielsetzung dieser Arbeit einerseits die Untersuchung verschiedener Methoden zur Messung des Automatisierungsvertrauens des Fahrers sowie andererseits die Identifikation, prototypische Umsetzung und Bewertung potentieller Ansätze zur Gestaltung von Automatisierungsvertrauen im Kontext von Systemen zum hochautomatisierten Fahren. Zu diesem Zweck wurden drei Fahrsimulatorstudien mit insgesamt N = 280 Probanden durchgeführt. Die vorliegenden Ergebnisse weisen darauf hin, dass (i) sowohl Selbstberichtsverfahren als auch Verhaltensmaße prinzipiell dazu verwendet werden können um das Automatisierungsvertrauen des Fahrers in Systeme zum hochautomatisierten Fahren zu operationalisieren, (ii) eine vorherige Auseinandersetzung mit funktionalen Grenzen von Systemen zum hochautomatisierten Fahren einen nachhaltigen Effekt auf das Automatisierungsvertrauen des Fahrers in das System haben kann und (iii) insbesondere Informationen über die Funktionsweise von Systemen zum hochautomatisierten Fahren das Automatisierungsvertrauen des Fahrers in derartige Systeme verbessern können. Damit liefert die vorliegende Arbeit sowohl wertvolle Ansatze zur Messbarmachung als auch Hinweise für die Gestaltung von Automatisierungsvertrauen im Kontext des hochautomatisierten Fahrens. Darüber hinaus können die Befunde dieser Arbeit in gewissem Maße auch auf andere Arten von Fahrzeugautomatisierung sowie unterschiedliche Domänen und Anwendungen von Automatisierung übertragen werden. / Automated driving systems allow to transfer a certain degree of vehicle control from the driver to a vehicle. By assisting, augmenting or even supplementing the driver, automated driving systems have been associated with enormous potential for improving driving safety, comfort, and efficiency - provided that they are used appropriately. Among those systems, conditional automated driving systems are particularly challenging for human-automation interaction: While the driver is no longer required to permanently monitor conditional automated driving systems, he / she is still expected to provide fallback performance of the dynamic driving task after adequate prior notification. Therefore, facilitating appropriate automation trust is a key component for enabling successful cooperation between drivers and conditional automated driving systems. Earlier work indicates that providing drivers with proper information about conditional automated driving systems might be one promising approach to do this. Considering the role of automation trust as a variable in the design of vehicle technology, however, also requires that drivers` automation trust can be viably measured in the first place. Accordingly, the objectives of this thesis were to explore difffferent methods for measuring drivers` automation trust in the context of conditional automated driving as well as the identification, implementation and evaluation of possible approaches for designing drivers` automation trust in conditional automated driving systems. For these purposes, three driving simulator studies with N = 280 participants were conducted. The results indicate that (i) both self-report measures and behavioral measures can be used to assess drivers` automation trust in conditional automated driving systems, (ii) prior familiarization with system limitations can have a lasting effffect on drivers` automation trust in conditional automated driving systems and (iii) particularly information about the processes of conditional automated driving systems might promote drivers` automation trust in these systems. Thus, the present research contributes much needed approaches to both measuring and designing automation trust in the context of conditional automated driving. In addition, the current findings might also be transferred to higher levels of driving automation as well as other domains and applications of automation.

[pt] Veículos autônomos ou Higly Automated Vehicles (HAVs) vêm trazendo novos paradigmas para o campo da ergonomia automotiva. A partir do momento em que motoristas se encontram fora de um loop contínuo de tomada de decisão, suas capacidades de retomada de controle manual do veículo durante situações de emergência são comprometidas. Para mitigar este problema, muitos autores acreditam que um maior entendimento dos padrões de aquisição de informação durante retomadas de controle em automação veicular pode fornecer insumos para a concepção de ferramentas designadas a auxiliar o motorista nesta tarefa, ao fornecer informações relevantes em momentos de necessidade. Baseado nestas questões, esta pesquisa visou categorizar o acesso de motoristas a diferentes informações oferecidas em interfaces de veículos autônomos durante a retomada de controle em diferentes níveis de automação. A pesquisa abordou o problema por meio de experimentos em simuladores de condução, onde motoristas foram expostos a diferentes cenários de retomada de controle, e seu seus padrões de olhar foram avaliados, para se testar a hipótese de que eles geralmente acessam a informação presente na interface apenas durante a retomada de controle em si, para checar o estado do sistema. Os resultados sugerem que o olhar do motorista está sujeito a influência de dois fatores: nível de automação e tarefa desempenhada. Foi observado que uma maior a quantidade de informação oferecida na interface aumenta concentração de olhares do motorista nesta região. Informações ativas sobre o ambiente melhoraram o desempenho do motorista durante as retomadas, porém tal benefício não se refletiu em uma maior usabilidade percebida. / [en] Highly automated vehicles (HAVs) are bringing new perspectives for the field of automotive ergonomics. By the time the driver is not constantly on the decision-making loop of the task, his/her performance for resuming control of the automation in safety-critical situations seems to be diminished. To mitigate this problem, many authors believe that by understanding drivers information scanning patterns and decision-making process during transitions of control in vehicle automation it is possible to design tools better adapted to support them in this activity, by providing relevant information in appropriate times. Based on this issue, this research aimed to categorize driver s reliance on the different information provided by the system s HMI during transitions of control in different levels of automation. The research followed a driving simulator experimental approach, where drivers were exposed to different take-over scenarios and their gaze behaviour was measured to test the hypothesis that they generally rely on information on the road to gain situation awareness, and only access the information on the HMI in cases of transitions of control, to check the system status. The results suggest that driver s gaze behaviour patterns are susceptible to influence of two main factors: the level of automation and the task in hand. It was observed that the more information presented on the HMI, the more drivers will look at it. Active information about the road environment have enhanced drivers performance during transitions of control, but it was not reflected in terms of perceived usability of the systems.