Machine learning is a powerful method to handle the self-driving problem. Researchers use machine learning to construct a neural network and train it to drive the car. A self-driving car is a safety-critical system. However, the neural network is not necessarily reliable. The output of a neural network can be easily influenced by many factors, such as the quality of training data and the runtime environment. Also, it takes time for the neural network to generate the output. That is, the self-driving car may not respond in time. Such weaknesses will increase the risk of accidents. In this thesis, considering the safety of self-driving cars, we apply a delay-aware shielding mechanism to the neural network to protect the self-driving car. Our approach is an improvement based on previous research on runtime safety enforcement for general cyber-physical systems that did not consider the delay to generate the output. Our approach contains two steps. The first is to use formal language to specify the safety properties of the system. The second step is to synthesize the specifications into a delay-aware enforcer called the shield, which enforces the violated output to satisfy the specifications during the whole delay. We use a lane keeping system as a small but representative case study to evaluate our approach. We utilize an end-to-end neural network as a typical implementation of such a lane keeping system. Our shield supervises those outputs of the neural network and verifies the safety properties during the whole delay period with a prediction. The shield can correct it if a violation exists. We use a 1/16 scale truck and construct a curvy lane to test our approach. We conduct the experiments both on a simulator and a real road to evaluate the performance of our proposed safety mechanism. The result shows the effectiveness of our approach. We improve the safety of a self-driving car and we will consider more comprehensive driving scenarios and safety features in the future. / Master of Science / Self-driving cars is a hot topic nowadays. Machine learning is a popular method to achieve self-driving cars. Machine learning constructs a neural network, which imitates a human driver's behavior to drive the car. However, a neural network is not necessarily reliable. Many things can mislead the neural network into making wrong decisions, such as insufficient training data or a complex driving environment. Thus, we need to guarantee the safety of self-driving cars. We are inspired to use formal language to specify the safety properties of the self-driving system. A system should always follow those specifications. Then the specifications are synthesized into an enforcer called the shield. When the system's output violates the specifications, the shield will modify the output to satisfy the specifications. Nevertheless, there is a problem with state-of-the-art research on specifications. When the specifications are synthesized into a shield, it does not consider the delay to compute the output. As a result, the specifications may not be always satisfied during the period of the delay. To solve such a problem, we propose a delay-aware shielding mechanism to continually protect the self-driving system. We use a lane keeping system as a small self-driving case study. We evaluate the effectiveness of our approach both on the simulation platform and the hardware platform. The experiments show that the safety of our self-driving car is enhanced. We intend to study more comprehensive driving scenarios and safety features in the future.

Indiana University-Purdue University Indianapolis (IUPUI) / Self-driving is simply the capacity of a vehicle to drive itself without human intervention. To accomplish this, the vehicle utilizes mechanical and electronic parts, sensors, actuators and an AI computer. The on-board PC runs advanced programming, which permits the vehicle to see and comprehend its current circumstance dependent on sensor input, limit itself in that climate and plan the ideal course from point A to point B. Independent driving is not an easy task, and to create self-sufficient driving arrangements is an exceptionally significant ability in the present programming designing field. ROS is a robust and versatile communication middle ware (framework) tailored and widely used for robotics applications. This thesis work intends to show how ROS could be used to create independent driving programming by investigating self-governing driving issues, looking at existing arrangements and building up a model vehicle utilizing ROS. The main focus of this thesis is to develop and implement a one-tenth scale of an autonomous RACECAR equipped with Jetson Nano board as the on-board computer, PCA9685 as PWM driver, sensors, and a ROS based software architecture. Finally, by following the methods presented in this thesis, it is conceivable to build an autonomous RACECAR that runs on ROS. By following the means portrayed in this theory of work, it is conceivable to build up a self-governing vehicle.

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.

A living vehicle sets forth the ability for a lifestyle not of a static place, but as part of the interstate system built into the American landscape. A living vehicle provides the ability to craft a lifestyle around mobility, and will support the situation of living on the road for extended periods of time with many potential benefits over traditional travel. First and foremost, a living vehicle gives the individual the ability to travel large spans with relative ease. A living vehicle's architecture will also provide the interior environment that supports the necessities and tasks of daily life similar to a house. This enables the individual to complete tasks in their living vehicle, as they traditionally would in their house, with the options available in the living vehicle to self drive and wirelessly charge all while remaining on the road. / Master of Architecture / A living vehicle sets forth the ability for a lifestyle not of a static place, but as part of the interstate system built into the American landscape. A living vehicle provides the ability to craft a lifestyle around mobility, and will support the situation of living on the road for extended periods of time with many potential benefits over traditional travel. First and foremost, a living vehicle gives the individual the ability to travel large spans with relative ease. A living vehicle's architecture will also provide the interior environment that supports the necessities and tasks of daily life similar to a house. This enables the individual to complete tasks in their living vehicle, as they traditionally would in their house, with the options available in the living vehicle to self drive and wirelessly charge all while remaining on the road.

abstract: Laser radars or lidar’s have been used extensively to remotely study winds within the atmospheric boundary layer and atmospheric transport. Lidar sensors have become an important tool within the meteorology and the wind energy community. For example, Doppler lidars are used frequently in wind resource assessment, wind turbine control as well as in atmospheric science research. A Time of Flight based (ToF) direct detection lidar sensor is used in vehicles to navigate through complex and dynamic environments autonomously. These optical sensors are used to map the environment around the car accurately for perception and localization tasks that help achieve complete autonomy. This thesis begins with a detailed discussion on the fundamentals of a Doppler lidar system. The laser signal flow path to and from the target, the optics of the system and the core signal processing algorithms used to extract velocity information, were studied to get closer to the hardware of a Doppler lidar sensor. A Doppler lidar simulator was built to study the existing signal processing algorithms to detect and estimate doppler frequency, and radial velocity information. Understanding the sensor and its processing at the hardware level is necessary to develop new algorithms to detect and track specific flow structures in the atmosphere. For example, the aircraft vortices have been a topic of extensive research and doppler lidars have proved to be a valuable sensor to detect and track these coherent flow structures. Using the lidar simulator a physics based doppler lidar vortex algorithm is tested on simulated data to track a pair of counter rotating aircraft vortices. At a system level the major components of a time of flight lidar is very similar to a Doppler lidar. The fundamental physics of operation is however different. While doppler lidars are used for radial velocity measurement, ToF sensors as the name suggests provides precise depth measurements by measuring time of flight between the transmitted and the received pulses. The second part of this dissertation begins to explore the details of ToF lidar system. A system level design, to build a ToF direct detection lidar system is presented. Different lidar sensor modalities that are currently used with sensors in the market today for automotive applications were evaluated and a 2D MEMS based scanning lidar system was designed using off-the shelf components. Finally, a range of experiments and tests were completed to evaluate the performance of each sub-component of the lidar sensor prototype. A major portion of the testing was done to align the optics of the system and to ensure maximum field of view overlap for the bi-static laser sensor. As a laser range finder, the system demonstrated capabilities to detect hard targets as far as 32 meters. Time to digital converter (TDC) and an analog to digital converter (ADC) was used for providing accurate timing solutions for the lidar prototype. A Matlab lidar model was built and used to perform trade-off studies that helped choosing components to suit the sensor design specifications. The size, weight and cost of these lidar sensors are still very high and thus making it harder for automotive manufacturers to integrate these sensors into their vehicles. Ongoing research in this field is determined to find a solution that guarantees very high performance in real time and lower its cost over the next decade as components get cheaper and can be seamlessly integrated with cars to improve on-road safety. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2018

The rapid pace of the development of the transport and vehicle industry in combination with megatrends such as digitalization, automation, and electrification can have huge effects on how transport planning and the society evolves. In order to meet goals such as increased traffic safety,improved environment, and reduced congestions a lot needs to be done. Two tools expected to be of significance when creating a more transport efficient society are automation and digitalization, whereby self-driving vehicles (SDVs) is an important area. The race towards fully autonomous vehicles is ongoing and scholars argue that the implementation of SDVs can be faster within freight transportation than passenger transportation. Higher costsavings, as well as decreasing availability on the labor market, are two arguments for why freighttransportation can be autonomous faster. Depending on how ambitious or slow the policy and planning are as well as the development of shared solutions, different future scenarios, as well as penetration rates of SDVs, can come through. One certain trend argued to continue to grow as well as having an impact on the development of SDVs is the rapid growth of e-commerce. This study addresses the uncertainty concerning SDVs from a transport administrator’s perspective by identifying important factors for Trafikverket regarding the implementation of SDVs within freight transportation. Four already developed future plausible scenarios for the year 2030 lay the ground for this study and a case study concerning the supply chain of e-commerce in Sweden is used to delimitate the study. Interviews with distributors were held to conduct the case and two workshops with experts within the transport sector, academia, and authorities, as well as a meeting with a reference group with representatives from Trafikverket were held to collect data. In the workshops, the experts identified trends and system impacts within the four future scenarios. A key insight gained in this study is that SDVs is an area with a lot of insecurity and thus, it needs to be investigated further. One solution to study the subject further is to implement restricted lanes for SDVs to test the technique properly. The results of this study clearly show that even though SDVs is a topical issue, it should not be studied as a solitary subject but rather in a larger context together with other significant factors. Nighttime transports and deliveries, platooning, and electricroads and electric vehicles are three factors that are likely to be implemented very soon and should, therefore, be studied together with SDVs. Moreover, the result from the workshops implies that there will be an increased number of vehicles as well as vehicle kilometers within the distribution of e-commerce packages in the future. In addition, the experts expect SDVs to be present in the year 2030, but the number of SDVs depend on multiple factors.

This thesis aims to create a first draft of a value-driven business model describing a mobility service for areas outside cities, which uses self-driving vehicles. The methodology used to fulfil the aim is based on service design thinking. User studies are conducted using qualitative interviews to explore the mobility needs and behaviour in rural areas. This is then combined with a morphological analysis, which is used as a structuring method for creating new business model concepts for the mobility service. Finally, stakeholder interviews are conducted in order to revise the developed business model and to find out their opinions about the proposed mobility service. The resulting mobility service is a feeder-service that includes self-driving vehicles, operated by the public transport authority. The study shows that a concept with self-driving vehicles like this would meet the users' mobility needs. Regarding the implementation of the service, stakeholders involved have driving factors that could facilitate the implementation, such as cost savings, increased accessibility, rural development, and environmental aspects. However, some barriers are identified as well, that mainly concerns the sparse structure and long distances in rural areas, the dimension of the vehicle fleet, laws and regulations, but also the psychological barriers such as acceptance of the users to go from using their own car to utilize self-driving vehicles in a mobility service.

This thesis examines how legal liability may be assessed in the case of an autonomous vehicle collision. It begins with a comprehensive discussion of autonomous vehicles: their defining features, a history of their development, and their current technological challenges. This paper later discusses the benefits of autonomous vehicles and why a new legal framework for their commercial production is necessary to realize these benefits. In doing so, it analyzes recent legislative efforts surrounding autonomous vehicles and their implications. Lastly, it utilizes the current product liability regime and precedent set by previous semi-autonomous vehicle collisions to suggest how liability may be determined in future legal suits.

Background: Nowadays, logistics service providers face several challenges which create an urge to rethink their strategy to improve their position within the market,decrease their costs and their environmental impact. At the same time theintroduction of autonomous driving potentially has an impact on logistics.Self-driving trucks can help logistics companies to tackle these challenges.However, the implementation of this technology could fundamentally alterthe competitive landscape. Hence, certain competitive advantages currentlyheld by logistics firms might lose their relevance in the future and need tobe adapted to maintain a strong market position. Purpose: The purpose of this study is to explore the perception of self-driving trucks within logistics and the impact on competitive advantages of logistics service providers. Thereby, this thesis will look at experts from Germany and Sweden and their opinion on future implications of self-driving trucks. Method: An inductive research approach is used to explore the topic. A multi-method research strategy is applied to gather data through qualitative semi-structured interviews with 17 participants. These were divided into five different case groups. To interpret the data a thematic analysis approach was chosen. Conclusion: The main contribution is a model representing the impact of autonomous driving on competitive advantages and the implications for the logistics industry. Findings are based on the perception of experts about autonomous driving, current resources and capabilities.

In an ever-changing industry where competition between actors is growing, technical improvements and investments can be a way to outperform competitors and gain competitiveadvantages. In a relatively under-developed industry, technological developments may leadto major improvements and change the layout of the whole business. Purpose – The main purpose of this thesis is to investigate potential cost reductions obtained by autonomous vehicles within the Swedish logistics industry. Studying opportunities for companies tostrengthen their competitive advantage can create new markets, chances or ensure a strongmarket position. To investigate said opportunities, the following research questions werestated: What is the actual cost of implementing an autonomous vehicle? Which costs will be affected by an implementation of autonomous vehicles? How do these costs impact the Swedish logistics market seen from a cost perspective? Method – The data necessary to answer the questions was collected from document studies, literature studies and interviews. These were carried out simultaneously in an iterativeprocess. Moreover, a pragmatic philosophy was undertaken, together with an abductive approach. The data was compared with existing theory by pattern matching and analysed withthematic approach, in order to ensure the level of trustworthiness. Findings/Implications – The findings of this thesis is that autonomous vehicles willheavily impact the logistics industry. By gradually implementing autonomous vehicles, theSwedish logistics sector can save upwards of 13,4 billion SEK between 2020 and 2030.This shift towards autonomous vehicles will move jobs from the long haul sector to urbanlogistics and telecommunications. Additionally, the society will see great benefits as 90% ofall traffic accidents will not happen when all vehicles are autonomous. It is clear that theSwedish logistics industry will benefit from an implementation of autonomous vehicles.Simultaneously it will also be beneficial for the society and the Swedish welfare. Limitations – The major limitation of this thesis is the time horizon. Because of being future oriented, much data was based on external estimations that might change over time.Moreover, only costs directly connected to transportations has been investigated, leavingroom for further studies related to indirect costs, as well as the organizational impacts onfuture supply chains.