Nowadays, autonomous driving revolution is getting closer to reality. To achieve the Autonomous driving the first step is to develop the Advanced Driver Assistance System (ADAS). Driver-assistance systems are one of the fastest-growing segments in automotive electronics since already there are many forms of ADAS available. To investigate state of art of development of ADAS towards Autonomous Driving, we develop Systematic Mapping Study (SMS). SMS methodology is used to collect, classify, and analyze the relevant publications. A classification is introduced based on the developments carried out in ADAS towards Autonomous driving. According to SMS methodology, we identified 894 relevant publications about ADAS and its developmental journey toward Autonomous Driving completed from 2012 to 2016. We classify the area of our research under three classifications: technical classifications, research types and research contributions. The related publications are classified under thirty-three technical classifications. This thesis sheds light on a better understanding of the achievements and shortcomings in this area. By evaluating collected results, we answer our seven research questions. The result specifies that most of the publications belong to the Models and Solution Proposal from the research type and contribution. The least number of the publications belong to the Automated…Autonomous driving from the technical classification which indicated the lack of publications in this area.

To achieve the widespread diffusion of autonomous electric buses (AEBs) and thus harness their environmental potential, a broad acceptance of new technology-based mobility concepts must be fostered. Still, there remains little known about the factors determining their acceptance, especially in the combination of vehicles with alternative fuels and autonomous driving modes, as is the case with AEBs. In this study, we first conducted qualitative research to identify relevant factors influencing individual acceptance of autonomously driven electric buses. We then developed a comprehensive research model that was validated through a survey of 268 passengers of an AEB, operated in regular road traffic in Germany. The results indicate that a mix of individual factors, social impacts, and system characteristics determine an individual’s acceptance of AEBs. Notably, it is important that users perceive AEBs, not only as advantageous, but also trustworthy, enjoyable, and in a positive social light. Our research supplements the existing corpora by demonstrating the importance of individual acceptance and incorporating it to derive policy implications.

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Sensors, as a crucial part of autonomous driving, are primarily used for perceiving the environment. The recent deep learning development of different sensors has demonstrated the ability of machines recognizing and understanding their surroundings. Automotive radar, as a primary sensor for self-driving vehicles, is well-known for its robustness against variable lighting and weather conditions. Compared with camera-based deep learning development, Object detection using automotive radars has not been explored to its full extent. This can be attributed to the lack of public radar datasets. In this thesis, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-EyeView range map. To build the dataset, we propose an instance-wise auto-annotation algorithm. Furthermore, a novel Range-Azimuth-Doppler based multi-class object detection deep learning model is proposed. The algorithm is a one-stage anchor-based detector that generates both 3D bounding boxes and 2D bounding boxes on Range-AzimuthDoppler and Cartesian domains, respectively. Our proposed algorithm achieves 56.3% AP with IOU of 0.3 on 3D bounding box predictions, and 51.6% with IOU of 0.5 on 2D bounding box predictions. Our dataset and the code can be found at https://github.com/ZhangAoCanada/RADDet.git.

This thesis is concerned with model predictive control (MPC) within the field of autonomous driving. MPC requires a model of the system to be controlled. Since a vehicle is expected to handle a wide range of driving conditions, it is crucial that the model of the vehicle dynamics is able to account for this. Differences in road grip caused by snowy, icy or muddy roads change the driving dynamics and relying on a single model, based on ideal conditions, could possibly lead to dangerous behaviour. This work investigates the use of Gaussian processes for learning a model that can account for varying road friction coefficients. This model is incorporated as an extension to a nominal vehicle model. A double lane change scenario is considered and the aim is to learn a GP model of the disturbance based on previous driving experiences with a road friction coefficient of 0.4 and 0.6 performed with a regular MPC controller. The data is then used to train a GP model. The GPMPC controller is then compared with the regular MPC controller in the case of trajectory tracking. The results show that the obtained GP models in most cases correctly predict the model error in one prediction step. For multi-step predictions, the results vary more with some cases showing an improved prediction with a GP model compared to the nominal model. In all cases, the GPMPC controller gives a better trajectory tracking than the MPC controller while using less control input.

Semi-autonomous driving is an emerging – though not unprecedented – technology which cannot necessarily be seen as safe and reliably accident-free. Insurance companies thus play an important role as influential stakeholders in the negotiation and implementation processes around this new technology. They can either push the technology (e.g. by offering beneficial, promotional insurance models for semi-autonomous car owners) or constrain it (e.g. by providing restrictive insurance models or no insurance cover at all). Insurers face questions concerning ethical or societal consequences on various levels: not only when it comes to promoting the technology – whose impact is not yet certain and may range from saving to endangering lives – but also with respect to insurance models such as “pay as you drive”, which may involve discriminatory elements. The concept of responsible research and innovation (RRI) is well suited to accompanying and guiding insurers, policy makers and other stakeholders in this field through a responsible negotiation process that may prove beneficial for everyone. Part of the RRI approach is to make stakeholders aware of “soft” factors such as the ethical, societal or historical factors which influence innovation and of the need to include these aspects in their activities responsibly.

The latest trends in autonomous vehicles research gave rise to the needs for specific tools to validate and test such systems. The estimations state that to consider an autonomous vehicle statistically safe, it should drive for thousands of kilometres using traditional validation methods. This process would take a long time. Furthermore, an update in the software, would require to re-run those kilometres. Therefore, the testing must be performed exploiting virtual simulations that should realistically reflect the real world. One way to perfor msuch simulations is to let the vehicle model drive down a road map and control the surrounding traffic. To be effective, spawned traffic should not be generated too far from the target vehicle. The OpenSCENARIO standard offers a feature restricting such traffic within an ellipse centred in the central object (target vehicle). This thesis investigated what technique was more efficient and scalable to detect viable roads within the ellipse to spawn stochastic traffic on. The explored solutions are two: an analytical approach and an adaptation of the AABB tree algorithm. The research started with simple cases and incremented the scenario’s complexity during the development. Through this methodology, each technique’s positive aspects and limits have been highlighted, allowing a comparison to be made. / De senaste trenderna i autonoma fordon har ökat behovet av specifika verktyg för att validera och testa sådana system. För att kunna betrakta ett autonomt fordon som statistiskt säkert, ska enligt uppskattningar autonoma fordon köra tusentals kilometer med traditionella valideringsmetoder. Denna process skulle ta mycket lång tid. Dessutom skulle en uppdatering i mjukvaran kräva att alla dessa tusentals kilometer att körs igen. Därför måste testningen utföras med hjälp av virtuella simuleringar som bör efterlikna den reella världen realistiskt. Ett sätt att genomföra dessa simuleringar är att låta en autonom fordonsmodell köra genom ett vägnät och kontrollera kringliggande trafik. För att vara effektiv, bör kringliggande trafik inte genereras för långt bort från autonoma fordonsmodellen. OpenSCENARIO-standarden innehåller en funktion som begränsar genererad trafik inom en ellips centrerad kring fordonsmodellen. Detta examensarbete undersökte vilka tekniker som är mest effektiva och skalbara för att detektera relevanta vägar inom ellipsen att generera stokastisk trafik på. De två lösningar som studerades var: en analytisk och en numerisk som använde sig av AABB-träd-algoritmen. Utförandet började med simpla fall som successivt ökade till mer avancerade scenarion. Genom denna metodik blev varje tekniks positiva aspekter samt begränsningar belysta och jämförbara.

Seeking a valuable and relevant topic for the future of mobility. the author came across the pain point trust in relation to artificial intelligence. Advances in the creation of artificial intelligence and deep learning ensure that our everyday lives are increasingly shaped by algorithms, sometimes consciously, sometimes unconsciously.For many people, this idea causes discomfort, and especially in situations of one‘s own vulnerability, the question of how an AI will handle more responsible tasks in the future will be essential. The automotive industry will also be shaped by this issue. In the intelligent car of the future, people will at least partially relinquish both control and privacy. Autonomous driving will be a test of trust for future users, as will the question of digital ethics and the collection of private data. In this thesis, a possible answer to the question was explored, how we can shape the approach and interaction with technology - especially artificial intelligence - in the future in order to create trustf uluser experiences. For this purpose, beyond the formal-aesthetic elaboration, the main focus was on interactive solutions and communication with AI, how an AI behaves in the vehicle and how it can contribute to making users feel comfortable in such a context. BMW Me&AI describes a scenario in which potential customers get to know an intelligent vehicle for the first time and are carefully introduced to its processes and possibilities. Inspired by soft robotics, the presented interior design is mainly defined by a holistic concept of soft interaction surfaces. Three basic scenarios are described in which passengers have the freedom to either look over AI‘s shoulder, sit back and focus on other things, or be completely on their own. This created a result that became unique in its dynamics and degree of adaptability and posed a real challenge, especially for the creative process, which in retrospect clearly paid off.

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A transformative revolution in transportation is coming with the advent of Au-tonomous Vehicles (AVs), which are expected to increase mobility, reduce trafficcongestion, and save fuel. Although AVs present significant advantages, they alsopose substantial challenges, particularly when it comes to security and safety. Theaim of this study is to map out the existing knowledge in order to facilitate furtherresearch and development, which will hasten the rollout of secure and reliable au-tonomous vehicles. This, in turn, will enable a sustainable and efficient future fortransportation. Research on AV safety and security is reviewed in this thesis in acomprehensive systematic literature review. The search process identified a total of283 studies published between 2019 and 2022, out of which 24 studies were selectedthrough a multi-stage process according to our predefined protocol. Based on re-search topics in selected studies, our findings have a significant impact on the fieldof Artificial Intelligence and automated vehicles. Based on our findings, we canprovide a summary of current knowledge regarding the safety, security, and stabilityimplications of autonomous vehicles. Simulations, real-life experiments, and physi-cal tests were all used in the selected articles for evaluation. Aside from the excellentresults, we identified many limitations of the articles, including the limitations of thedata sets, the analysis of unusual events, and the verification practices.