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Before vehicles that go to sale for public it goes through many stages of testing in different fields. All types of pre-production cars are the vehicles that come after prototypes which allow the OEM to find out possible issues by running different categories of tests. One of the most important fields of test is regarding safety. When it comes to autonomous cars, where safety is crucial, the number of tests to carry out, time for handling them and their complexity is increased. To reduce costs, complexity and time some of them might be simulated. After the analysis is done the cars have to be tested in real time to collect more data, not only from car sensors but also through observation from the tests filmed. This thesis contains an overview of whole project leading to develop an autonomous platform, driving after autonomous car and controlled by simulation environment and solving major issues of the project. The filming of autonomous cars remotely had two major issues. One of the issues was to integrate all the different software and platforms. The trajectories of the autonomous cars were controlled in different software and the trajectories of the RC were managed in different software. The solution to integrate all the software and platforms is shown in this thesis. The second issue was to make the RC car to follow the waypoints that are generated by simulation and by communicating with real car using a PID controller. The project is prepared for future improvements, like installing a camera to the RC car which will follow predefined test independently.

Driverless vehicles might fundamentally change the transport system in multiple ways. Reducing driver costs in mobility services could create opportunities for new mobility concepts. Research on driverless vehicles have previously concentrated on urban areas, though driverless vehicles in rural areas could have greater positive effects. Hence, the aim of the study is to see how driverless vehicles can be used in rural areas to contribute to a more sustainable transport system. Three rural mobility concepts for driverless vehicles are developed and by applying these to different case locations, the feasibility of the concepts is discussed. Interviews with local actors in Sweden were conducted to learn about general and local challenges with specific case locations. What rural mobility concept for driverless vehicles to use depends on access to public transport, distance to main roads and spatial density of travel demand. A modelling approach of a first and last mile feeder service is used to evaluate the feasibility of this mobility concept further. Model results show that driverless shuttles can feed travel demands of 100-150 passengers daily and still perform alternative tasks. Even though rural areas have general challenges, local issues also need consideration to optimize the benefits of the services. Public transport authorities are experts on local challenges and could take more responsibility in questions regarding driverless vehicles. For instance, flexibility, accessibility and equality could be improved by merging routes and shorten travel times for entire bus routes. Furthermore, other societal functions can be developed by reinvesting capital in other areas.

The automobile is a cultural artifact embedded in our lives and imbued with meaning. Autonomous vehicle technology stands to alter not just the way we drive or whether we drive, it also has the power to fundamentally change the way we live. The development of driverless cars enables the examination of the complex relationships that individuals have with the automobile and reveals the fears associated with this technological change.

Emerging automotive and transportation technologies, such as autonomous vehicles (AVs) have created revolutionary possibilities in the way we might travel in the future. Major car manufacturers and technology giants have demonstrated significant progress in advancing and testing AV technologies in real-life traffic conditions. Results from multi-population surveys indicate that despite enjoying moderate familiarity with AVs, more than 40% of the respondents were likely to use them when they become available. Simply looking at the demographic differences without paying any regard to the perceptions might suggest that the demographic differences are the primary causal factors behind the differences observed in the intended adoption of AVs. This study investigates the role of demographics and other factors (current travel characteristics, crash history and familiarity with AVs) on consumers’ perceptions and intended adoption of AVs with a view of disentangling one factor from the other. Results show that the observed demographic differences in intended adoption rates are due to demographic differences in the perceptions on the benefits and concerns of AVs. The study outcomes suggest that it may be beneficial to first address consumers’ perceptions on the benefits and concerns regarding AVs. The results from this study can be used to inform modeling decisions and policy discussions relevant to future market penetration of AV technology.

Emerging automotive and transportation technologies have provided revolutionary possibilities in the way we might travel in the future. Major car manufacturers and technology giants have demonstrated significant progress in advancing and testing autonomous vehicles in real-life traffic conditions. Governmental agencies are grappling with how to plan transportation systems for a world with autonomous vehicles. Past research has shown that not all technologies are immediately welcomed by the public. Autonomous Vehicles would have to likely go through a similar phase, and would need to overcome not just technological challenges but also social barriers for successful penetration into the marketplace. Most previous studies on consumer opinions, and potential adoption of these technologies provide only descriptive, univariate analyses that fail to extract deeper insights on consumers’ perceptions, and their intended adoption of autonomous vehicles. Multi-population surveys were conducted to obtain data on consumers’ perceptions, their intended adoption, and eventual use of autonomous vehicles. Descriptive results revealed that around one-fifth of the respondents were unfamiliar about this technology, with larger shares of the younger generations expressing unfamiliarity. Questions on intended adoption of autonomous vehicles were asked across two stages of the survey and results revealed the merit of providing information to the recipients which seem to have assisted them in making more informed decisions about their intended adoption (or non-adoption process). 40% of the respondents were unlikely to adopt autonomous vehicles with a further 20% being unsure, presently. When analyzed across generations, it was seen that higher shares of older generations were unlikely to adopt autonomous vehicles than their younger counterparts. In addition to adoption, other interesting insights on use of autonomous vehicles, and travel behavioral implications of autonomous vehicles were also obtained in this analysis. Considering the vast market potential of this technology, it is important to obtain insights on possible differences in adoption (or non-adoption) across various consumer market segments. The current dissertation fills these gaps in the literature by providing an in-depth understanding of the potential market segments of autonomous vehicle consumers, and revealing the factors influencing their adoption (or non-adoption of autonomous vehicles). Two-step cluster analysis of consumers’ perceptions of potential benefits and concerns with autonomous vehicles reveal four distinct consumer market segments – the benefits-dominated market segment, the concerns-dominated market segment, the uncertain market segment, and the well-informed market segment. The insights obtained are further used to uncover various triggers influencing the adoption (or non-adoption) of autonomous vehicles across these market segments. It can be seen that in addition to the influence of sociodemographics, various other factors such as current travel characteristics, crash history, and current vehicle purchase inventory have significant influences in the adoption process across each market segment. The results from this exercise provide autonomous vehicle stakeholders with a more in-depth understanding of the potential market segments interested (or uninterested) in adopting autonomous vehicles, which could be used to develop enhanced marketing and policy initiatives to achieve better outcomes. Considering the high initial cost of autonomous vehicles, novel business models like shared autonomous vehicles (SAVs), could emerge as possible alternatives to individually owning, and operating autonomous vehicles. The recent emergence of popular rideshare giants, such as Uber and Lyft, into the SAV market have further brought some discussion on possible alterations to household vehicle ownership models in a shared environment. Previous research simulating SAV fleets in a gridded city network reveal the cost benefits of having shared autonomous vehicles in comparison to owning and individually operating them. This study looks into the implications of shared autonomous vehicles on current household vehicle ownership and uncovers the factors influencing the relinquishment of a household vehicle to use shared autonomous vehicles for commute trips. Results show that the effect of relinquishing household vehicles is different among single- and multi-vehicle households with different triggers such as socio-demographics, current travel characteristics, crash severities, and vehicle purchase histories influencing the relinquishment of household vehicles.

Software has never been as important as today – and its impact on life, work and society is growing at an impressive rate. We are in the flow of a software-induced transformation of nearly all aspects of our way of life and work. The dependence on software has become almost total. Malfunctions and unavailability may threaten vital areas of our society, life and work at any time. The two massive challenges of software are one hand the complexity of the software and on the other hand the disruptive environment. Complexity of the software is a result of the size, the continuously growing functionality, the more complicated technology and the growing networking. The unfortunate consequence is that complexity leads to many problems in design, development, evolution and operation of software-systems, especially of large software-systems. All software-systems live in an environment. Many of today’s environments can be disruptive and cause severe problems for the systems and their users. Examples of disruptions are attacks, failures of partner systems or networks, faults in communications or malicious activities. Traditionally, both growing complexity and disruptions from the environment have been tackled by better and better software engineering. The development and operating processes are constantly being improved and more powerful engineering tools are introduced. For defending against disruptions, predictive methods – such as risk analysis or fault trees – are used. All this techniques are based on the ingenuity, experience and skills of the engineers! However, the growing complexity and the increasing intensity of possible disruptions from the environment make it more and more questionable, if people are really able to successfully cope with this raising challenge in the future. Already, serious research suggests that this is not the case anymore and that we need assistance from the software-systems themselves! Here enters “autonomic computing” – A promising branch of software science which enables software-systems with self-configuring, self-healing, self-optimization and self-protection capabilities. Autonomic computing systems are able to re-organize, optimize, defend and adapt themselves with no real-time human intervention. Autonomic computing relies on many branches of science – especially computer science, artificial intelligence, control theory, machine learning, multi-agent systems and more. Autonomic computing is an active research field which currently transfers many of its results into software engineering and many applications. This Hauptseminar offered the opportunity to learn about the fascinating technology “autonomic computing” and to do some personal research guided by a professor and assisted by the seminar peers.

Software has never been as important as today – and its impact on life, work and society is growing at an impressive rate. We are in the flow of a software-induced transformation of nearly all aspects of our way of life and work. The dependence on software has become almost total. Malfunctions and unavailability may threaten vital areas of our society, life and work at any time. The two massive challenges of software are one hand the complexity of the software and on the other hand the disruptive environment. Complexity of the software is a result of the size, the continuously growing functionality, the more complicated technology and the growing networking. The unfortunate consequence is that complexity leads to many problems in design, development, evolution and operation of software-systems, especially of large software-systems. All software-systems live in an environment. Many of today’s environments can be disruptive and cause severe problems for the systems and their users. Examples of disruptions are attacks, failures of partner systems or networks, faults in communications or malicious activities. Traditionally, both growing complexity and disruptions from the environment have been tackled by better and better software engineering. The development and operating processes are constantly being improved and more powerful engineering tools are introduced. For defending against disruptions, predictive methods – such as risk analysis or fault trees – are used. All this techniques are based on the ingenuity, experience and skills of the engineers! However, the growing complexity and the increasing intensity of possible disruptions from the environment make it more and more questionable, if people are really able to successfully cope with this raising challenge in the future. Already, serious research suggests that this is not the case anymore and that we need assistance from the software-systems themselves! Here enters “autonomic computing” – A promising branch of software science which enables software-systems with self-configuring, self-healing, self-optimization and self-protection capabilities. Autonomic computing systems are able to re-organize, optimize, defend and adapt themselves with no real-time human intervention. Autonomic computing relies on many branches of science – especially computer science, artificial intelligence, control theory, machine learning, multi-agent systems and more. Autonomic computing is an active research field which currently transfers many of its results into software engineering and many applications. This Hauptseminar offered the opportunity to learn about the fascinating technology “autonomic computing” and to do some personal research guided by a professor and assisted by the seminar peers.:Introduction 5 1 What Knowledge Does a Taxi Need? – Overview of Rule Based, Model Based and Reinforcement Learning Systems for Autonomic Computing (Anja Reusch) 11 2 Chancen und Risiken von Virtual Assistent Systemen (Felix Hanspach) 23 3 Evolution einer Microservice Architektur zu Autonomic Computing (Ilja Bauer) 37 4 Mögliche Einflüsse von autonomen Informationsdiensten auf ihre Nutzer (Jan Engelmohr) 49 5 The Benefits of Resolving the Trust Issues between Autonomic Computing Systems and their Users (Marc Kandler) 61

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Assuming that vehicle connectivity technology matures and connected vehicles hit the market, many of the running vehicles will be equipped with highly sophisticated sensors and communication hardware. Along with the goal of eliminating human distracted driving and increasing vehicle automation, it is necessary to develop novel intersection control strategies. Accordingly, the research presented in this dissertation develops an innovative system that controls the movement of vehicles using cooperative cruise control system (CACC) capabilities entitled: iCACC (intersection management using CACC). In the iCACC system, the main assumption is that the intersection controller receives vehicle requests from vehicles and advises each vehicle on the optimum course of action by ensuring no crashes occur while at the same time minimizing the intersection delay. In addition, an innovative framework has been developed (APP framework) using the iCACC platform to prioritize the movements of vehicles based on the number of passengers in the vehicle. Using CACC and vehicle-to-infrastructure connectivity, the system was also applied to a single-lane roundabout. In general terms, this application is considered quite similar to the concept of metering single-lane entrance ramps. The proposed iCACC system was tested and compared to three other intersection control strategies, namely: traffic signal control, an all-way stop control (AWSC), and a roundabout, considering different traffic demand levels ranging from low to high levels of congestion (volume-to-capacity ration from 0.2 to 0.9). The simulated results showed savings in delay and fuel consumption in the order of 90 to 45 %, respectively compared to AWSC and traffic signal control. Delays for the roundabout and the iCACC controller were comparable. The simulation results showed that fuel consumption for the iCACC controller was, on average, 33%, 45% and 11% lower than the fuel consumption for the traffic signal, AWSC and roundabout control strategies, respectively. In summary, the developed iCACC system is an innovative system because of its ability to optimize/model different levels of vehicle automation market penetrations, weather conditions, vehicle classes/models, shared movements, roundabouts, and passenger priority. In addition, the iCACC is capable of capturing the heterogeneity of roadway users (cyclists, pedestrians, etc.) using a video detection technique developed in this dissertation effort. It is anticipated that the research findings will contribute to the application of automated systems, connected vehicle technology, and the future of driverless vehicle management. Finally, the public acceptability of the new advanced in-vehicle technologies is a challenging task and this research will provide valuable feedback for researchers, automobile manufacturers, and decision makers in making the case to introduce such systems. / Ph. D.