The urban population is growing at an exponential rate throughout the world leading to the problems related to swift and speedy mobility or issues caused by convectional mobility options. This study illustrates and explores the new ways to transport people specially taking into account the self-driving cars concept and discusses the concept of mobility 4.0 (smart / intelligent mobility) and briefly highlights the technological aspects of autonomous vehicles, adaptation advantages and progress in laws and legislations of autonomous vehicle. The study is primarily qualitative and relies on the work of previous researcher, technical reports and blogs but the part of this study is quantitative where empirical data was collected from the experts in a conference held by BBG Austria. The result of the studies shows adaptation readiness of Austrian professional market and business prospects associated with autonomous vehicles Moreover, different business models are suggested, which could be adopted to incorporate the driverless vehicles in day-to-day life of an individual living in urban environment. The models basically suggest that the adaptation of the technology would help curbing transport externalities especially external cost associated to transportation of each individual; which includes congestion, accident, infrastructure costs and environmental costs which are incurred by least efficient conventional cars and would also help shrinking the diseases like premature mortality, aggravation of respiratory as well as cardiovascular disease and sleep disturbance which are the result of city level congestion and pollution. (author's abstract) / Series: Schriftenreihe des Instituts für Transportwirtschaft und Logistik - Verkehr

The future of mobility is changing at an exponential rate, as every day passes it moves closer to the goal of complete autonomy, therefore, it is safe to say that the adaptation of self-driving cars in near future is no more a matter of science fiction. Keeping in view the rapid evolution of mobility, this study tries to enlighten and compile the importance of autonomous vehicles in our daily life by highlighting monetary and societal advantages of adaptation. Moreover, it analysis the Austrian expert's opinion on the topic of adaptation of the self- driving cars and business prospects with autonomous vehicles for current businesses. The data on these two questions was collected in a workshop using Delphi method, where experts from varied professional backgrounds participated. But mainly the participants of the controlled group were representatives of public procurement, energy and automobile industry/sector. After a detailed presentation and discussion over the topic, the participants were asked for their views on forth mentioned two questions. Their opinions were recorded and visualized on a custom made graph and further analyzed using the descriptive statistical tool. At the end of the question and answer session a vast majority of experts, which is approximately 80%, thought that there is a good possibility of adaptation of autonomous vehicles in near future. But on the other hand relatively less percentage of experts were confident about the bright future for current businesses in automobile industry; They argued that the current business decorum would change dramatically in a couple of decades and this would be the question of survival of the fittest and smartest.

Wide spread consumer adoption of self-driving cars (SDC) is predicated on a level of trust between humans and the autonomous vehicle. Despite advances being made in the technical abilities of SDCs, recent studies indicate that people are negatively predisposed toward utilizing autonomous vehicles. To bridge the gap between consumer skepticism and adoption of SDCs, research is needed to better understand the evolution of trust between humans and growing autonomous technologies. The question of mainstream acceptance and requisite trust is explored through integration of virtual reality SDC simulator, an electroencephalographic (EEG) recorder, and a new approach for real-time trust measurement between passengers and SDCs. An experiment on fifty human subjects was conducted where participants were exposed to scenarios designed to induce positive and negative trust responses. Emotional state was quantified by the EEG beta wave to alpha wave power ratio, and participants self-reported their levels of trust in the SDC after each segment. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Automotive manufacturers are competing to be the first to introduce customer-ready autonomous vehicles. Some manufacturers are claiming to launch their first self-driving cars as early as 2020. Which all sounds very good and futuristic; however, the question arises, are customers even ready to adopt this new technological advancement? Therefore, this pilot study is aimed at finding out the answer to this question in the Austrian market. This study discovers the standpoint of Austrian consumers concerning the acceptance of self-driving cars for daily usage and gives an overview of the current point of view regarding autonomous vehicles (AVs). The data for this study was collected using an online, user-friendly, Likert scale survey. The collected data were processed and analyzed for empirical significance in SPSS using Spearman's rank correlation and the Mann-Whitney U test supported by descriptive analysis. The results of the study indicate that Austrian consumers are well aware of autonomous vehicles and their technology. However, they have specific concerns about reliability, cybersecurity, and futuristic car-sharing models. Therefore, these concerns about AVs should be addressed by auto manufactures in order to gain consumers' trust and sell them a new form of mobility.

In this thesis, I argue that a neo-Marxist critical theory perspective on self-driving cars shifts critical conversations from risks and benefits to concerns about the commodification of free time necessary for our human experience of autonomy. First, I outline that neo-Marxist perspective by charting the different types of power exercised by a capitalist in order to increase their surplus. I then analyze Karl Marx's conception of time in economic exchange to show that, under capitalism, power is exercised over labor through the commodification of workers' free time. I then introduced Michel Foucault's concept of biopower to transition to the commodification not only of labor but also of bodies. Then, I introduce contemporary German philosopher Byung-Chul Han's concept of psychopolitics as a neo-Marxist critique of the exercise of power over the psyche of individuals in order to increase their surplus. These philosophers' models shift commodification from labor to bodies to information. In the final section, I apply Han's contemporary critique of power dynamics to the case of self-driving cars (SDCs) to show that the technologies they represent may serve to perpetuate the negative implications of a constantly optimizing society: a continuation of commodification of the very conditions of labor. This analysis illuminates an overlooked possible negative implication of this emerging technology, as contemporary literature focuses heavily on the developer of the self-driving cars rather than the user and glosses over possible concerns of alienation of the workers' time itself. I argue that increases in "free time" proposed by the implementation of self-driving cars will inevitably be used for "auto"-exploitation, or, self-exploitation. This thesis will contribute to developing work on the effects self-driving cars have on their users, rather than emphasizing effects on society or our environments.

This degree project investigates the suitability of using Behavior Trees (BT) as an architecture for the behavioral layer in autonomous driving. BTs originate from video game development but have received attention in robotics research the past couple of years. This project also includes implementation of a simulated traffic environment using the Unity3D engine, where the use of BTs is evaluated and compared to an implementation using finite-state machines (FSM). After the initial implementation, the simulation along with the control architectures were extended with additional behaviors in four steps. The different versions were evaluated using software maintainability metrics (Cyclomatic complexity and Maintainability index) in order to extrapolate and reason about more complex implementations as would be required in a real autonomous vehicle. It is concluded that as the AI requirements scale and grow more complex, the BTs likely become substantially more maintainable than FSMs and hence may prove a viable alternative for autonomous driving.

No description available.

Automated vehicles are those which are capable of sensing their environments in order to perform at least some aspects of the safety-critical control (like steering, throttling, or braking) without direct human input. As a guide for planners and policymakers, the objective of this thesis is to develop a strong foundation for anticipating the potential impacts resulting from advancements in vehicle automation. To establish the foundation, this thesis uses a robust qualitative methodology, coupling a review of literature on the potential advantages and disadvantages of vehicle automation and lessons from past innovations in transportation, with recent trends of the Millennial Generation, carsharing services, and a series of interviews with thought-leaders in automation, planning, policymaking, transportation, and aviation. Five significant findings emerged from this thesis: (1) the impacts of vehicle automation differ depending on one’s visions of what automation means, how it is implemented, what the automation does, and where it operates; (2) current limitations of vehicle automation to perform all aspects of the dynamic driving task in all driving conditions make it difficult to move from level-4 to level-5 automation; (3) level-5 automation is required to have any effect on carsharing, mobility, and quality of life; (4) assuming effective planning and policymaking techniques, housing preferences, urban growth, and increases in total VMT will likely not be significantly impacted by vehicle automation; (5) human drivers may never be allowed to disengage their attention from a partially-automated vehicle, specifically in applications where drivers are expected to reengage their attention in safety-critical situations. From the perspective of understanding the bigger picture, this thesis developed a proposed future scenario of vehicle automation in the next five to ten years that is used to suggest guiding principles for policymakers, and key recommendations for planners, engineers, and researchers.

Autonomous vehicles (AVs) have become increasingly prevalent in modern society, with multiple companies pursuing avenues to advance technology. Despite the promises of safer driving, mitigating accidents and reducing stress, mixed opinions exist on the reliability and trustworthiness of the technology. While many factors form opinions regarding AVs, the component of personal values in AV discourse has not been well-documented. This research focuses on understanding which personal values are most prominent in AV discourse and how individuals prioritize these personal values. We studied two AV-focused data sources to gain perspective: 24 TED Talk transcripts and 20,000 Reddit user posts. Next, we scored the prevalence of personal values using the Personal Values Dictionary (PVD). Our results found that self-direction, achievement, power, stimulation, and conformity had an overall positive inclination in AV discourse. However, we also found value conflicts between both data sets, indicating a potential dissonance between professional opinions and those of typical consumers. Our findings provide insight into values literature and issues consumers bring up when discussing or adopting AVs. Our research on value conflicts can also link to consumers' potential adoption of AVs. Thus, we provide potentially useful perspectives for companies to address these concerns directly in their AV production, advertising, and design philosophies.

Machine perception is a key element for the research on autonomous driving vehicles. In particular, we focus on the problem of lane detection with a single camera. Many lane detection systems have been developed and many algorithms have been published over the years. However, while they are already commercially available to deliver lane departure warnings, their reliability is still unsatisfactory for fully autonomous scenarios. In this work, we questioned the reasons for such limitations. After examining the state of the art and the relevant literature, we identified the key methodologies adopted. We present a self-standing discussion of bird’s eye view (BEV) warping and common image preprocessing techniques, followed by gradient-based and color-based feature extraction and selection. Line fitting algorithms are then described, including least squares methods, Hough transform and random sample consensus (RANSAC). Polynomial and spline models are considered. As a result, a general processing pipeline emerged. We further analyzed each key technique by implementing it and performing experiments using data we previously collected. At the end of our evaluation, we designed and developed an overall system, finally studying its behavior. This analysis allowed us on one hand to gain insight into the reasons holding back present systems, and on the other to propose future developments in those directions. / Thesis / Master of Science (MSc)