Conselho Nacional de Desenvolvimento Científico e Tecnológico / Semi-Structured Information Retrieval is an intermediate way to retrieve information between Textual Retrieval and Structured Retrieval (typical in relational database systems). In structured retrieval systems, users generally know the available data structure and query languages, so they can formulate queries that produce more accurate results. In textual retrieval users dont known the data structure and formulate queries with keywords only, which produces not so accurate results. In Semi-Structured Retrieval, users generally dont known the data structure and formulate queries that mix textual search and structured retrieval mechanisms. In this context, the problem of how to improve the results accuracy using the structure inside semi-structured documents appears. Semi-structured data is usually stored as XML documents and can be seen as trees. Internal nodes of these trees have the structure of documents, while leaf nodes contain text. The design of interfaces for users in this context is one of the biggest challenges in semi-structured information retrieval. This occurs especially because the users dont known the document structure and have problems in formulating structured queries. This dissertation presents a proposal and a prototype interface developed to help users in the process of formulation of structured queries. The aim is to increase the precision in the results of the queries. The proposal is validated by experiments involving volunteers users and by comparing the results of textual queries and structured queries made with the help of the interface. The improvement reaches 440% for well structured queries, with a user who knows the interface, and 179.75% for reasonably structured queries, by users without experience to use the interface. / A Recuperação Semi-Estruturada é uma forma de recuperação de informação intermediária entre a Recuperação Textual e a Recuperação Estruturada (típica em sistemas de banco de dados relacionais). Em sistemas de recuperação estruturada, o usuário geralmente conhece a estrutura dos dados e as linguagens de consulta disponíveis, conseguindo assim formular consultas que produzem resultados mais precisos. Na Recuperação Textual o usuário não conhece a estrutura dos dados e formula as consultas apenas com palavraschaves, as quais geram resultados não tão precisos. Na Recuperação Semi-Estruturada, o usuário geralmente desconhece a estrutura dos dados e formula consultas que mesclam buscas textuais e mecanismos de recuperação estruturada. Neste contexto, surge o problema de como melhorar a precisão dos resultados aproveitando a estrutura contida nos documentos semi-estruturados. Dados semi-estruturados são comummente armazenados como documentos XML, os quais podem ser vistos como árvores. Nós internos dessas árvores contem a estrutura do documento enquanto os nós folhas contêm os dados. O projeto de interfaces para usuários neste contexto é um dos grandes desafios na recuperação semi-estruturada. Isso ocorre especialmente porque os usuários não conhecem a estrutura do documento e têm dificuldade na formulação de consultas estruturadas. Este trabalho apresenta uma proposta e um protótipo de interface desenvolvido para auxiliar os usuários no processo de formulação de consultas estruturadas. Pretende-se com isso aumentar a precisão nos resultados das consultas. A proposta é validada por meio de experimentos envolvendo usuários voluntários e pela comparação de resultados obtidos com consultas textuais e consultas estruturadas formuladas com o auxílio da ferramenta. A melhoria atinge 440% para consultas bem estruturadas, realizadas por usuário que conhece bem a interface, e 179,75% para consultas razoavelmente estruturadas, realizadas por usuários sem experiência no uso da interface. / Mestre em Ciência da Computação

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As cycling becomes more popular and automated driving is on the rise, it can be assumed that in the city of the future highly automated vehicles (HA Vs) and cyclists will share the same roads. Yet only little is known about how cyclists announce their maneuvers to motorized vehicles or how they communicate and interact with them. Knowledge on these aspects is currently missing to guide the design of cyclist-HA V interactions. Situations where a cyclist rides upfront a vehicle, will be especially challenging for HA Vs, such as when a cyclist (A) avoids an obstacle on the road section ahead, (B) merges onto the road from an ending cycling path, or (C) leaves the road turning into a driveway {see Figure 1) [1 ]. Based on the cyclist's intention, the HA V will have to pass or keep following with only limited options to communicate to the cyclist ahead. Design solutions derived from the well-studied field of pedestrian-HA V interactions cannot simply be transferred to the here considered cyclist-HA V interactions, since in past research successful design concepts for pedestrians were not beneficial for cyclists [2]. Hence, it is vital to investigate the behavior and experiences of cyclists in more detail and to explore possible design solutions for HA V interaction behavior in these situations. With this study we aim to get more insights into the subjective experience of cyclists travelling in longitudinal traffic, especially during cyclist-vehicle interactions, as well as to derive cyclists' requirements to design safe and desirable cyclist-HA V interactions.

Automated vehicles (AVs) will be integrated into our society at some point in the future, but when is still up for debate. An extensive amount of research is being completed to understand the communication methods between AVs and other road users sharing the environment to prepare for this future. Currently, researchers are working to understand how different forms of external communication on the AVs will impact vulnerable road user (VRU) interaction. However, within the last 10 years, VRU casualty rates have continued to rise for all classifications of VRUs. Unfortunately, there is no suggestion that pedestrian fatality rates will ever decrease without some intervention. This dissertation aims at understanding the impacts of eHMI across real-world, complex scenarios with AVs and how researchers can apply those future findings to improve VRUs' judgments to today. A series of studies evaluated the necessity and impact of eHMI on AV–VRU interaction, assessed how the visual components of eHMI influenced VRU crossing decisions, and how variations in a real-world environment (multiple vehicles and scenario complexity) impact crossing decision behavior. Two studies examined how eHMI will impact future interactions between AVs and VRUs. Specifically, to understand how to advance the design of these future devices to avoid unintended consequences that may result. Results from these studies found that the presence and condition of eHMI did not influence participants' willingness to cross. Participants primarily relied on the speed and distance of the vehicle to make their crossing decision. It was difficult for participants to focus on the eHMI when multiple vehicles competed for their attention. Participants typically prioritized their focus on the vehicle that was nearest and most detrimental to their crossing path. Additionally, the type of scenario caused participants to make more cautious crossing decisions. However, it did not influence their willingness to cross. The last study applied the learnings from the first two studies to a foundational perception study for current legacy vehicles. These results showed a significant increase in judgment accuracies with a display. Through analysis across overall conclusions from the 3 studies, five critical findings were identified when addressing eHMI and 3 design recommendations, which are discussed in the penultimate section of this work. The results of this dissertation indicate that eHMI improved VRUs' accuracy of perception of change in vehicle speed. eHMI did not significantly impact VRUs crossing decisions. However, the complexity of the traffic scenarios affected the level of caution participants exhibited in their crossing behavior. / Doctor of Philosophy / An extensive amount of research is being completed to understand the communication methods between AVs and other road users sharing the environment to prepare for this future. Currently, researchers are working to understand how different forms of external communication on the AVs will impact vulnerable road user (VRU) interaction. However, within the last 10 years, VRU casualty rates have continued to rise for all classifications of VRUs. Unfortunately, there is no suggestion that pedestrian fatality rates will ever decrease without some intervention. This dissertation aims at understanding the impacts of eHMI across real-world, complex scenarios with AVs and how researchers can apply those future findings to improve VRUs' judgments to today. A series of studies evaluated the necessity and impact of eHMI on AV–VRU interaction, assessed how the visual components of eHMI influenced VRU crossing decisions, and how variations in a real-world environment (multiple vehicles and scenario complexity) impact crossing decision behavior.

The study aimed to identify and analyse methods for establishing external communication between humans and autonomous/teleoperated machines/vehicles using various Human-Machine Interfaces (HMIs). The study was divided into three phases. The purpose of the first phase was to identify and highlight previously tested/researched methods for establishing external communication by conducting a literature review. The findings from the literature review were categorised into six points of interest: machine indications, test delivery methods, HMI technologies/types, symbols, textual/numerical messages, and colours associated with different indications. Based on these findings, four HMIs (projection, display, LED-strip, and auditory) were selected for evaluation in a virtual reality environment for the second phase of the study, which has the purpose of identifying which of the human-machine interfaces can effectively communicate the intentions of autonomous/teleoperated machines to humans. The results of phase two indicate that the participants preferred projection as the most effective individual HMI, and when given the option to combine two HMIs, projection combined with auditory was the most preferred combination. The participants were also asked to pick three HMIs of their choosing, resulting in the projection, display and audible HMI combination being the preferred option. The evaluation of HMIs in a virtual reality environment contributes to improving dependability and identifying usability issues.  The objective of the third and final phase was to gather all the findings from the previous phases and subsequently refine the report until it was considered finalised. Future work includes enhancing the realism of the VR environment, refining machine behaviour and scenarios, enabling multiple participants to simultaneously interact with the environment, and exploring alternative evaluation methods. Addressing these areas will lead to more realistic evaluations and advancements in human-machine interaction research.